Prediction of the 70-gene signature (MammaPrint) high versus low risk by nomograms among axillary lymph node positive (LN+) and negative (LN-) Chinese breast cancer patients, a retrospective study

doi:10.21203/rs.3.rs-4977540/v1

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Prediction of the 70-gene signature (MammaPrint) high versus low risk by nomograms among axillary lymph node positive (LN+) and negative (LN-) Chinese breast cancer patients, a retrospective study

https://doi.org/10.21203/rs.3.rs-4977540/v1

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Background: Luminal-type breast cancer (BC) was characterized as hormonal receptor positive human epidermal receptor 2 negative (HR+/HER2-), which comprises the majority of breast cancer (BC). The 70-gene signature (70-GS, MammaPrint) test is recommended for assessing recurrence risk and guiding adjuvant chemotherapy decisions in Luminal-type BC. Based on our previously established nomogram models for predicting binary categorized risk of 70-GS, this study aims to update nomogram models to predict binary 70-GS risk for lymph node positive (LN+) and lymph node negative (LN-) luminal-type BC patients.

Methods: This retrospective study included 301 consecutive female patients with HR+/HER2- BC treated at Peking Union Medical College Hospital from November 2019 to December 2023. Patients' medical history, imaging reports, and clinicopathological features were reviewed. Forty risk parameters were compared between 70-GS high vs. low-risk patients among LN+ and LN- groups. High risk stratification criterion in MonarchE and Natalee were compared between low and high 70-GS risk for the first time. Logistic regression was utilized to establish nomogram models predicting binary 70-GS risk for LN+ and LN- patients. The models' prediction performance was evaluated using accuracy, AUC of ROC curves, C-index, calibration curves, and decision curve analysis.

Results: Significant differences were found in several risk parameters between 70-GS high vs. low-risk patients in both LN+ and LN- groups. Among LN+ patients, parameters including childbirth number (p=0.024), cardiovascular diseases (p=0.037), US min. diameter of tumor (p=0.034), Ki67 index (p<0.001) and PR positivity (p=0.007) were significant predictors. Among LN- patients, micro-calcifications (p=0.011), PR positivity (p=0.021), and Ki67 index (p<0.001) were significant. The nomogram models showed high predictive accuracy, with AUC of 0.948 in the training set (C-index 0.948, 0.914-0.982, accuracy 0.907) and 0.923 in the testing set (C-index 0.923, 0.919-0.927, accuracy 0.828) for LN+ patients and 0.917 in the training set (C-index 0917, 0.861-0.972, accuracy 0.870) and 0.917 in the testing sets (C-index 0917, 0.912-0.922, accuracy 0.808) among LN- patients. Calibration plots and decision curve analysis demonstrated the models' reliability and clinical utility.

Conclusions: Our updated nomogram models for predicting 70-GS risk in LN+ and LN- luminal-type BC patients demonstrated improved prediction performance. The models facilitate individualized risk assessment and treatment decision-making, highlighting the distinct risk factor distributions between LN+ and LN- patients. These findings support the use of tailored approaches in managing luminal-type BC based on lymph node status.

breast cancer

70-gene signature (MammaPrint)

lymph node positive

lymph node negative

nomogram

risk prediction

Breast cancer (BC) is the most commonly diagnosed cancer among women worldwide [1]. In China, BC is the leading cause of cancer-related death among women under the age of 45 [2–4]. The luminal-type, characterized by hormone receptor-positive, human epidermal receptor 2-negative (HR+/HER2-) status, comprises approximately 60% of all BC cases [5]. Multiple gene assays are commonly employed to evaluate recurrence risk, the benefit of adjuvant chemotherapy, the necessity of neoadjuvant treatment, and the potential benefit of adjuvant ribociclib in early BC [6–12].

The 70-gene signature (70-GS, MammaPrint) test is one of the gene expression assays recommended by the National Comprehensive Cancer Network (NCCN) guidelines for luminal-type BC [13]. In the MINDACT trial, patients with a clinical high risk but a 70-GS low risk could safely avoid chemotherapy [6]. For BC patients with ultra-low risk results from the 70-GS test, prognostic survival was excellent, and they may be candidates for de-escalating treatment options, such as shortening the duration of endocrine therapy [14, 15]. Conversely, BC patients with high and ultrahigh risk according to the 70-GS test were associated with higher pathological complete response (pCR) rates following neoadjuvant chemotherapy and could determine chemosensitivity and survival outcomes as predictive and prognostic biomarkers in the I-SPY2 trial [11, 16]. Thus, studies have shown that the 70-GS test enhances both physician and patient confidence in making decisions regarding adjuvant chemotherapy [17, 18].

Previously, we established a nomogram model based on individualized medical history, imaging features, and clinicopathological characteristics to predict the binary (high/low) and quartile-categorized (ultrahigh, high, low, ultralow) risk according to the 70-GS test in luminal-type BC among Chinese patients, with acceptable predictive performance [19]. However, the nomogram was built on data from only 150 patients, regardless of the presence of metastasis to the axillary lymph node (LN), which is considered one of the most important prognostic factors in luminal-type BC. The TAILORx and RxPONDER trials, as well as real-world studies, revealed that the distribution of risk factors, the potential benefit of chemotherapy, and treatment decision-making all differ between lymph node-positive (LN+) and lymph node-negative (LN-) patients [7, 20–22]. Furthermore, LN metastasis plays a crucial role in determining the benefit of adjuvant abemaciclib according to the MonarchE trial, whereas in stage II LN- (pT2N0) BC patients, the 70-GS risk should also be considered in evaluating the benefit of adjuvant ribociclib according to the Natalee trial [12, 23, 24].

In the current study, we aim to establish updated nomogram models to predict binary 70-GS risk (high/low) for LN + and LN- luminal-type BC patients, respectively, using data from 301 consecutive patients, and to investigate whether the predictive performance of the nomograms can be improved in this manner.

Ethics statement

This retrospective study was approved by the Ethics Committee of the Peking Union Medical College Hospital (PUMCH), Chinese Academy of Medical Sciences (No. K3610).

Patient Population

A total of 301 consecutive female patients diagnosed with HR+/HER2- BC received treatment in the Department of Breast Surgery at PUMCH between November 2019 and December 2023. Patients’ medical histories, ultrasound (US) and mammogram (MG) imaging reports, and clinicopathological features were reviewed and collected. The 70-GS assay was performed by ZhenHe Genecast Biotechnology, the exclusive appointed partner for the 70-GS assay (MammaPrint) in China, designated by Agendia.

Comparison of Imaging and Clinicopathological Risk Parameters between 70-GS High- vs. Low-Risk Patients among LN + and LN- Groups

Forty parameters were compared, including patients’ medical history factors, imaging features from MG and US reports, and clinicopathological characteristics, first between the 70-GS high-risk (N = 73) and low-risk (N = 102) women among LN + patients (N = 175), and second between the 70-GS high-risk (N = 63) and low-risk (N = 63) women among LN- patients (N = 126). Imaging features, including MG density, microcalcifications, nodule/mass presence, breast imaging reporting and data system (BI-RADS) category, US aspect ratio, boundary, morphology, hyperechogenicity, multicentricity/multifocality, blood flow, lymph node condition, and US BI-RADS category, were extracted from imaging reports and coded for comparison as in the previous study. [19].

Comparisons of Risk Levels and Scores from Established Models between 70-GS High- and Low-Risk Patients in LN + and LN- Groups

Comparisons of risk levels and scores from established models including Adjuvant! Online (AOL) version 8.0 [25], CTS5 [26, 27], IHC3 [28], and the Nottingham prognostic index (NPI) [29] were performed firstly between the 70-GS high risk (N = 73) and low risk (N = 102) women among LN + patients (N = 175), and secondly between the 70-GS high risk (N = 63) and low risk (N = 63) women among LN- patients (N = 126) [19]. According to the most updated high-risk criteria for adjuvant abemaciclib treatment in the MonarchE trial, approved by the Food and Drug Administration (FDA) in the United States in March 2023 [30] and by the National Medical Products Administration (NMPA) in China in August 2023 [31, 32], and the high-risk criteria for adjuvant ribociclib treatment in the Natalee trial [12], the MonarchE (FDA), MonarchE (NMPA) and Natalee risk calculations were also compared.

Prediction of 70-GS Binary Risk with Nomogram Models Among LN + and LN- Patients’ Groups

The 175 LN + and LN- patients were randomly split into training and testing sets at a 4:1 ratio. Univariate analyses and multivariate logistic regression were performed based on binary 70-GS risk classification (high vs. low risk). Two nomograms were established to predict the binary risk categories of 70-GS for LN + and LN- patients, respectively. The predictive performance of the nomograms was evaluated using accuracy, area under the curve (AUC) of receiver operating characteristic (ROC) curves, and C-index with 95% confidence intervals (CI). Calibration curves and decision curve analysis (DCA) were used for visual inspection of calibration and to assess the potential clinical utility of the nomogram for binary risk prediction.

Statistical analysis

Categorical variables were compared using chi-square tests, while quantitative variables were analyzed with t-tests. Ordered rank data were compared using the Wilcoxon test or Kruskal-Wallis test. Parameters associated with 70-GS risk were identified through univariate analysis, and binary nomogram models were established using multivariate logistic regression. Risk predictors were selected using stepwise regression analyses based on the Akaike information criterion (AIC), clinical importance, and findings from our previous study. Statistical analyses were performed using R software (version 4.0.3). All statistical tests were two-sided, with statistical significance defined as a p-value < 0.05.

Comparison of Imaging and Clinicopathological Risk Parameters between 70-GS High- vs. Low-Risk Patients in LN + and LN- Groups

The workflow of our study was summarized in the Fig. 1. Patients’ imaging and clinicopathological risk parameters were collected and reviewed. Among the LN + women (N = 175), there were significant differences between 70-GS high- and low-risk patients in the number of childbirths, the percentage of patients with cardiovascular diseases, the presence of screen-detected non-palpable BC (NPBC), US minimum tumor diameter, US blood flow, pT stage, PR positivity percentage and level, HER2 FISH ratio, and Ki67 index (all p < 0.05) (Table 1). The 70-GS high-risk LN + patients had more children, fewer cardiovascular diseases, fewer screen-detected NPBC cases and more interval cancers, a greater minimum tumor diameter measured by US, more BC with disordered blood flow on US, fewer T1 stage BC cases, BC with lower PR positivity, a higher Her2 FISH ratio, and a higher Ki67 index (all p < 0.05) (Table 1).

Table 1

Comparison of medical history risk factors, imaging features and clinicopathological characteristics between Chinese patients with high versus low risk of 70-gene signature test
Clinicopathological and imaging characteristics	LN+(N = 175)
Clinicopathological and imaging characteristics	70-genehighrisk N = 73	70-genelowrisk N = 102	P-value		70-genehighrisk N = 63	70-genelowrisk N = 63	P-value
70-gene score (Mean ± SD)	-0.23 ± 0.21	0.23 ± 0.15	< 0.001		-0.30 ± 0.24	0.18 ± 0.14	< 0.001
Medical history factors
Age (Mean ± SD)	49.70 ± 10.14	51.87 ± 10.06	0.163		49.76 ± 11.91	50.24 ± 11.56	0.820
Age group			0.677				0.902
< 40	12 (16.4)	11 (10.8)			14(22.2)	12 (19.0)
40 ~ 49	29 (39.7)	39 (38.2)			18(28.6)	19 (30.2)
50 ~ 59	17 (23.3)	27 (26.5)			17 (27.0)	20 (31.7)
≥ 60	15 (20.5)	25 (24.5)			14 (22.2)	12 (19.0)
BMI (Mean ± SD)	24.12 ± 3.34	23.69 ± 2.84	0.372		23.06 ± 2.89	23.55 ± 2.66	0.327
Childbirth (Mean ± SD)	1.32 ± 0.72	1.08 ± 0.61	0.024		1.08 ± 0.52	1.02 ± 0.61	0.529
Age of menarche (Mean ± SD)	13.99 ± 1.68	13.74 ± 1.23	0.279		13.40 ± 1.30	13.57 ± 1.40	0.470
Age of menopause (Mean ± SD)	50.77 ± 3.81	51.39 ± 2.22	0.421		50.57 ± 2.34	50.04 ± 3.60	0.512
Family history of breast, ovarian and pancreatic cancer			0.259				0.544
No	67 (91.8)	88 (86.3)			58 (92.1)	56 (88.9)
Yes	6 (8.2)	14 (13.7)			5 (7.9)	7 (11.1)
Cardiovascular disease as co-morbidity			0.037				0.187
No	67 (91.8)	82 (80.4)			53 (84.1)	47 (74.6)
Yes	6 (8.2)	20 (19.6)			10 (15.9)	16 (25.4)
Screen-detected NPBC			0.028				0.526
No	59 (80.8)	67 (65.7)			50 (79.4)	47 (74.6)
Yes	14 (19.2)	35 (34.3)			13 (20.6)	16 (25.4)
Bilateral cancer			0.999^				0.999^
No	70 (95.9)	99 (97.1)			61 (96.8)	60 (95.2)
Yes	3 (4.1)	3 (2.9)			2 (3.2)	3 (4.8)
Imaging features
MG BI-RADS density				0.126^&				0.614^&
fatty	3 (4.1)	4 (3.9)			3 (4.8)	1 (1.6)
scattered	7 (9.6)	15 (14.7)			5 (7.9)	9 (14.3)
Heterogeneous	57 (78.1)	81 (79.4)			50 (79.4)	49 (77.8)
Dense	6 (8.2)	2 (2.0)			5 (7.9)	4 (6.3)
MG micro-calcification cluster			0.356				0.011
No	40 (54.8)	63 (61.8)			30 (47.6)	44 (69.8)
Yes	33 (45.2)	39 (38.2)			33 (52.4)	19 (30.2)
MG nodule/mass			0.130				0.108
No	16 (21.9)	33 (32.4)			13 (20.6)	21 (33.3)
Yes	57 (78.1)	69 (67.6)			50 (79.4)	42 (45.7)
MG lesion BI-RADS			0.201^				0.226^
2, 3 and 4a	12 (16.4)	24 (23.5)			10 (16.4)	21 (33.3)
4b and 4c	43 (58.9)	58 (56.9)			43 (70.5)	31 (49.2)
5 and 6	16 (21.9)	17 (16.7)			8 (13.1)	11 (17.5)
Unknown	2 (2.7)	3 (2.9)			2 (3.2)	0
US lesion BI-RADS			0.855^#&				0.810^#&
2, 3 and 4a	4 (5.5)	5 (4.9)			5 (7.9)	7(11.1)
4b and 4c	39 (53.4)	57 (55.9)			38 (60.3)	33 (52.4)
5 and 6	30 (41.1)	40 (39.2)			20 (31.7)	23 (36.5)
US max. diameter of tumor (Mean ± SD, cm)	2.15 ± 1.00	1.87 ± 0.94	0.064		2.16 ± 0.80	2.31 ± 1.44	0.464
US min. diameter of tumor (Mean ± SD, cm)	1.38 ± 0.66	1.18 ± 0.55	0.034		1.38 ± 0.50	1.34 ± 0.96	0.753
US diameter Ratio (min/max) (Mean ± SD)	0.67 ± 0.17	0.66 ± 0.15	0.639		0.66 ± 0.15	0.62 ± 0.18	0.172
US aspect ratio			0.590				0.526
Normal	59 (80.8)	79 (77.5)			47 (74.6)	50 (79.4)
Abnormal	14 (19.2)	23 (22.5)			16 (25.4)	13 (20.6)
US boundary			0.069				0.794
Clear	8 (11.0)	4 (3.9)			8 (12.7)	9 (14.3)
Unclear	65 (89.0)	98 (96.1)			55 (87.3)	54 (85.7)
US morphology			0.905				0.544
Regular	4 (5.5)	4 (3.9)			7 (11.1)	5 (7.9)
Irregular	69 (94.5)	98 (96.1)			56 (88.9)	58 (92.1)
US hyperechoicity			0.325				0.722
No	31 (42.5)	51 (50.0)			33 (52.4)	31 (49.2)
Yes	42 (57.5)	51 (50.0)			30 (47.6)	32 (50.8)
US focality			0.373				0.828
Unifocal	53 (72.6)	80 (78.4)			49 (77.8)	50 (79.4)
Multifocal	20 (27.4)	22 (21.6)			14 (22.2)	13 (20.6)
US blood flow			0.018^&				0.261^&
Normal	7 (9.6)	15 (14.7)			8 (12.7)	9 (14.3)
Peripheral	18 (24.7)	36 (35.3)			17 (27.0)	13 (20.6)
Internal Disorder	31 (42.5) 17 (23.3)	39 (38.2) 12 (11.8)			25 (39.7) 13 (20.6)	19 (30.2) 22 (34.9)
US lymph node			0.178^&				0.806^&
Normal	29 (39.7)	53 (52.0)			51 (81.0)	50 (79.4)
Suspected	26 (35.6)	27 (26.5)			8 (12.7)	8 (12.7)
Abnormal	18 (24.7)	22 (21.6)			4 (6.3)	5 (7.9)
Clinicopathological characteristics
Tumor histology			0.928				0.489
IDC-NOS	67 (91.8)	94 (58.4)			57 (90.5)	60 (95.2)
Other	6 (8.2)	8 (7.8)			6 (9.5)	3 (4.8)
pT			0.021^&				0.945^&
T1	46 (63.0)	81 (79.4)			38 (60.3)	38 (60.3)
T2	26 (35.6)	19 (18.6)			23 (36.5)	24 (38.1)
T3	1 (1.4)	2 (2.0)			2 (3.2)	1 (1.6)
Tumor volume (Mean ± SD, cm³)	30.54 ± 44.62	21.07 ± 44.78	0.169		25.32 ± 23.92	35.03 ± 96.56	0.441
Number of positive nodes			0.258				0.941
0	29 (39.7)	53 (52.0)			51 (81.0)	50 (79.4)
1	26 (35.6)	27 (26.5)			8 (12.7)	8 (12.7)
2	18 (24.7)	22 (21.6)			4 (6.3)	5 (7.9)
TNM stage			0.999^{^}				1^{^}
I	0	0			38 (60.3)	38 (60.3)
II III	72 (98.6) 1 (1.4)	100 (98.0) 2 (2.0)			25 (39.7) 0	25 (39.7) 0
Histological grade			0.928				0.489
G1	67 (91.8)	94 (92.2)			57 (90.5)	60 (95.2)
G2	6 (8.2)	8 (7.8)			6 (9.5)	3 (4.8)
LVI			0.893				0.455
No	61 (83.6)	86 (84.3)			52 (82.5)	55 (87.3)
Yes	12 (16.4)	16 (15.7)			11 (17.5)	8 (12.7)
ER positivity (%) (Mean ± SD)	87.23 ± 9.72	88.41 ± 6.96	0.377		87.40 ± 11.43	86.75 ± 9.03	0.724
ER positive level			0.143				1
Strong (3+)	43 (58.9)	74 (72.5)			40 (63.5)	40 (63.5)
Mild/Moderate (1–2+)	30 (41.1)	43 (58.9)			23 (36.5)	23 (36.5)
PR positivity (%) (Mean ± SD)	57.62 ± 35.40	71.11 ± 27.18	0.007		52.65 ± 34.51	66.30 ± 30.81	0.021
PR positive level			0.098				1
High (≥ 20%)	58 (87.9)	95 (96.0)			51 (94.4)	56 (94.9)
Low (< 20%)	8 (12.1)	4 (4.0)			3 (5.6)	3 (5.1)
PR positive level			0.019^&				0.130^&
Strong (3+)	55 (45.8)	95 (60.1)			25 (46.3)	36 (61.0)
Moderate (2+)	55 (45.8)	54 (34.2)			24 (44.4)	19 (32.2)
Mild (1+)	10 (8.3)	9 (5.7)			5 (9.3)	4 (6.8)
HER2			0.076^&				0.119^&
0	5 (7.6)	5 (5.1)			10 (15.9)	19 (30.2)
1+	31 (47.0)	35 (35.4)			27 (42.9)	23 (36.5)
2+	30 (45.5)	59 (59.6)			26 (41.3)	21 (33.3)
HER2 low			0.581				0.057
No	13 (17.8)	15 (14.7)			10 (15.9)	19 (30.2)
Yes	60 (82.2)	87 (85.3)			53 (84.1)	44 (69.8)
Her2 FISH ratio (Mean ± SD)	1.35 ± 0.31	1.12 ± 0.24	0.003		1.35 ± 0.25	1.42 ± 1.08	0.793
Ki67			< 0.001				< 0.001
High (≥ 20%)	50 (68.5)	24 (23.5)			48 (76.2)	25 (39.7)
Low (< 20%)	23 (31.5)	78 (76.5)			15 (23.8)	38 (60.3)
BC, breast cancer; SD, standard deviation; BMI, body mass index; NPBC, non-palpable breast cancer; MG, mammogram; US, ultrasound; BI-RADS, breast imaging reporting and data system; IDC-NOS, invasive ductal carcinoma not otherwise specified; TNM, tumor-node-metastasis; LVI, lymphovascular invasion; ER, estrogen receptor; PR, progesterone receptor; FISH, fluorescence in situ hybridization.
* The comparison was performed without the perimenopausal patients
^# The comparison was performed without unknown cases.
^ The comparison was performed by fisher test.
^$ The comparison was performed only for Her2 (2+) patients.
^&The comparison was performed by Wilcoxon test.

Among the LN- group of patients (N = 126), there were significant differences between 70-GS high- and low-risk patients in MG microcalcifications, PR positivity percentage, and Ki67 index (all p < 0.05) (Table 1). The 70-GS high-risk LN- patients had more BC with microcalcifications, BC with lower PR positivity, and a higher Ki67 index (all p < 0.05) (Table 1).

The parameters of PR positivity percentage and Ki67 were significantly different between 70-GS high- and low-risk patients in both LN + and LN- groups.

Comparisons of Risk Level and Score from Established Models between 70-GS High- vs. Low-Risk Patients in LN + and LN- Groups

Among the LN + group of patients (N = 175), there were significantly more patients classified as AOL high risk, CTS5 high risk, CTS5 intermediate risk, IHC3 high risk, MonarchE (FDA) high risk, and MonarchE (NMPA) high risk, with significantly higher CTS5 and NPI scores (Table 2). Since all patients were LN+, they were all classified as high risk by the Natalee criteria (Table 2).

Table 2

Comparison of risk calculated from established models between Chinese patients with high versus low risk based on binary risk classification of 70-gene signature test
Risk calculated from established models	LN+ (N = 175)
Risk calculated from established models	70-genehighrisk N = 73	70-genelowrisk N = 102	P-value	70-genehighrisk N = 63	70-genelowrisk N = 63	P-value
AOL			0.013			0.150
High	70 (95.9)	84 (82.4)		31 (49.2)	23 (36.5)
Low	3 (4.1)	18 (17.6)		32 (50.8)	40 (63.5)
CTS5			0.005			0.030
Score (Mean ± SD)	3.52 ± 0.51	3.27 ± 0.64		3.05 ± 0.52	2.83 ± 0.61
CTS5			0.012^&			0.016^&
High	19 (26.0)	19 (18.6)		1 (1.6)	2 (3.2)
Intermediate	34 (46.6)	35 (34.3)		32 (50.8)	21 (33.3)
Low	20 (27.4)	48 (47.1)		30 (47.6)	40 (63.5)
IHC3			< 0.001			0.001
High	31 (42.5)	8 (7.8)		30 (47.6)	13 (20.6)
Low	42 (57.5)	94 (92.2)		33 (52.4)	50 (79.4)
NPI			< 0.001			< 0.001
Score (Mean ± SD)	3.32 ± 0.38	2.06 ± 0.44		3.48 ± 0.49	2.20 ± 0.43
NPI			0.003^&			0.075^&
Poor	3 (4.1)	18 (17.6)		30 (47.6)	40 (63.5)
Moderate	67 (91.8)	83 (81.4)		33 (52.4)	23 (36.5)
Good	3 (4.1)	1 (1.0)		0	0
MonarchE (FDA)			0.066			1
High	8 (11.0)	3 (2.9)		0	0
Low	65 (89.0)	99 (97.1)		63 (100)	63 (100)
MonarchE (NMPA)			< 0.001			1
High	51 (69.9)	26 (25.5)		0	0
Low	22 (30.1)	76 (74.5)		63 (100)	63 (100)
Natalee			1			0.056
High	73 (100)	102 (100)		25 (39.7)	15 (23.8)
Low	0	0		38 (60.3)	48 (76.2)
AOL, Adjuvant! Online; CTS5, Clinical treatment Score post–5 years; IHC3, immunohistochemistry 3; NPI, Nottingham prognostic index
^&The comparison was performed by Wilcoxon test.
^The comparison was performed by Fisher test
*The comparison was performed by Chi square test of continuous correction.
FDA, Food and Drug Administration of the United States
NMPA, National Medical Products Administration of China

Among the LN- group of patients (N = 126), there were significantly more patients classified as CTS5 intermediate risk and IHC3 high risk, with significantly higher CTS5 and NPI scores (Table 2). The difference in the percentage of high-risk patients classified by the Natalee criteria was marginal (p = 0.056) (Table 2). Since all patients were LN-, they were all classified as low risk by the MonarchE criteria (Table 2).

Prediction of 70-GS Binary Risk with Nomogram Models in LN + and LN- Patient Groups

Among the LN + group of patients (N = 175), the risk parameters identified by logistic regression included age, body mass index (BMI), childbirth, cardiovascular disease as a comorbidity, bilateral BC, mammographic breast density, microcalcifications, US BI-RADS, US aspect ratio, US boundary, US morphology, US blood flow, BC histology, pT stage, PR positivity percentage, Her2-low status, and Ki67 (Fig. 2). The accuracy, AUC of ROC, and C-index (95% confidence interval) of the training and testing sets among LN + patients were 0.907 and 0.828, 0.948 and 0.923, and 0.948 (0.914–0.982) and 0.923 (0.919–0.927), respectively (Table 3, Fig. 3).

Table 3

Parameters includeaccuracy, area under curve (AUC) and C-index to evaluate the prediction performance and discrimination of binary and quartile categorized nomogram models
Nomogram models \ Parameters to evaluate the nomograms	LN+ (N = 175)		LN- (N = 126)
Nomogram models \ Parameters to evaluate the nomograms	Training set (N = 140)	Testing set (N = 35)	Training set (N = 100)	Testing set (N = 26)
Accuracy	0.907	0.828	0.870	0.808
AUC	0.948	0.923	0.917	0.917
C-index (95% CI)	0.948 (0.914–0.982)	0.923 (0.919–0.927)	0.917 (0.861–0.972)	0.917 (0.912–0.922)
AUC, area under curve; CI, confidence interval

Among the LN- group of patients (N = 126), the risk parameters identified by logistic regression included mammographic breast density, microcalcifications, US diameter ratio (min/max), US hyperechogenicity, US blood flow, PR positivity percentage, and Ki67 (Fig. 4). The accuracy, AUC of ROC, and C-index (95% confidence interval) of the training and testing sets among LN- patients were 0.870 and 0.808, 0.917 and 0.917, and 0.917 (0.861–0.972) and 0.917 (0.912–0.922), respectively (Table 3, Fig. 5).

The calibration plots indicated that the predicted 70-GS binary risk from the two nomograms showed fair consistency with the observed 70-GS results (Fig. 3, 5). The DCA indicated that when the threshold for predicted probability of high risk was within the range of 0.2–0.8, the nomogram model would provide more net benefit than an “all or none” strategy (Fig. 3, 5).

Prediction of 70-GS binary risk with nomogram models without separating LN + and LN- patients groups

After univariate and multivariate analysis, age, palpability, grade, PR positivity (%), and Ki67 were incorporated into the nomogram model to predict the binary risk classification of 70-GS (Supplementary Fig. 1). For binary risk classification prediction, the nomogram achieved an AUC of 0.853 (accuracy 0.739, C-index 0.853, 95% CI 0.806-0.900) in the training set and 0.779 (accuracy 0.750, C-index 0.779, 95% CI 0.662–0.896) in the testing set (Supplementary Fig. 2).

We identified the intersection of variables included in our previous nomogram model [19], the LN + nomogram model, and the LN- nomogram model (Fig. 6). PR positivity and Ki67 were selected as key variables.

While de-escalation in local therapy of BC, including surgery and radiotherapy, has been the standard of care for many years, chemotherapy decisions are largely individualized based on the personalized risk of the patient [33]. Multigene assays have been used to avoid chemotherapy in patients with luminal-type BC, even in those with nodal involvement [6, 20]. The 70-GS assay, which includes 70 genes associated with tumor progression and metastasis, was approved by the FDA in 2007 for predicting the risk of distant recurrence at 5 or 10 years in early BC patients [34, 35]. When it was first applied among 295 consecutive patients with early BC, showing a 10-year DMFS rate of 54% for high-risk and 94% for low-risk women, differences were observed in the clinicopathological features and treatment of the 144 LN + patients and 151 LN- patients included in the study [36]. The predictive ability of 70-GS for the long-term prognosis of BC patients with 1–3 positive LNs was validated in an independent study [37]. Following this, the inclusion criteria for the MINDACT trial were revised, and ultimately, 21% of the patients included in MINDACT were LN+ [6]. Indeed, the evaluation method for the potential benefit of adjuvant chemotherapy differs according to the TAILORx and RxPONDER trials, particularly in terms of LN status and the 21-gene recurrence score (RS). LN status is also a key indicator for adjuvant CDK4/6 inhibitors, such as abemaciclib and ribociclib [12, 23, 24]. Therefore, in the current study, we developed updated nomograms for LN + and LN- patients separately.

The user-friendly, integrated multifactor nomograms typically facilitate individualized risk evaluation and assist in the prompt selection of patients. Several studies have established nomograms predicting the 21-gene RS [38–40], while efforts to establish nomograms predicting 70-GS risk have been limited [41]. Lee et al. established a nomogram to predict the probability of 70-GS low risk in women with clinically high-risk BC, incorporating age, grade, PR, and Ki-67, all of which were included in our previous nomogram models, except for age [19]. Based on our previous work, we conducted the current study with a doubled cohort size of 301 consecutive BC patients, with particular emphasis on the key parameter of LN status. We hypothesized that the distribution of risk factors between LN + and LN- subgroups of BC patients might differ, and the candidate parameters for nomograms for LN + versus LN- women might also be distinct. The 70-GS high-risk LN + patients had more children, fewer cardiovascular diseases, more BC with disordered blood flow on US, fewer T1 stage BC cases, BC with lower PR positivity, and a higher Ki67 index (all p < 0.05) (Table 1). These six parameters were all included in the LN + nomogram (Fig. 2). Conversely, the 70-GS high-risk LN- patients had more BC with microcalcifications, BC with lower PR positivity, and a higher Ki67 index (all p < 0.05) (Table 1). These three parameters were all included in the LN- nomogram (Fig. 4). With regard to the risk parameters, PR positivity and the Ki67 index were the common factors included in all three nomograms (Fig. 6). Interestingly, comorbidity was included in both our previous nomogram and the LN + nomogram as a 'protective' factor, suggesting that patients with cardiovascular diseases might be considered low-risk rather than high-risk (Fig. 2, 6).

To mitigate potential bias resulting from sample size discrepancies, we increased the sample size from 150 to 301 and conducted nomograms that included all patients, regardless of their lymph node status. For the binary categorized risk nomogram model, the AUC of the ROC improved from 0.826 to 0.853 in the training set and from 0.737 to 0.779 in the testing set. However, compared to the AUC of the ROC (training 0.826, testing 0.737) and C-index (training 0.903, testing 0.785) of binary risk prediction from our previous nomogram, the prediction performance significantly improved with the nomograms established for LN + and LN- populations separately. Among LN + patients, the AUC (training 0.948, testing 0.923), accuracy (training 0.907, testing 0.828), and C-index (training 0.948, testing 0.923) showed marked improvement. Similarly, among LN- patients, the AUC (training 0.917, testing 0.917), accuracy (training 0.870, testing 0.808), and C-index (training 0.917, testing 0.917) also improved (Table 3). We focused on the binary prediction of 70-GS risk and did not develop nomograms for quartile prediction due to the limited number of cases classified into quartiles among LN + and LN- patients.

Lymph node metastasis is a significant prognostic factor for early BC (EBC) patients. We established nomograms predicting 70-GS risk on an individualized basis with acceptable accuracy; however, the nomogram model did not effectively distinguish between patient cohorts with and without lymph node metastasis [19]. Upon further analysis, separating patients with LN + from LN-, we observed that the accuracy of AOL risk stratification significantly improved for LN + patients in our study. The AOL for Breast Cancer tool, a free web-based prognostication tool used globally to estimate 10-year survival probabilities and assess the benefits of adjuvant therapy, has been employed to aid in clinical decision-making [42]. Our prior study indicated that 43 (28.7%) patients classified as low-risk by AOL were subsequently evaluated by their physicians and underwent the 70-GS test due to the overly optimistic survival assessments provided by AOL in Asian patients.[42]. In our study, only 21 (12%) LN + patients with AOL low-risk received the 70-GS, with only 3 (14.3%) patients were ultimately identified as 70-GS high risk patients, which demonstrated that as for LN + patients, it might be safer to exempt from 70-GS to alleviate the patients’ economic burden. However, for LN- patients, there was no significant difference in the proportion of high-risk patients identified by AOL between the binary risk groups of patients evaluated by a 70-GS, indicating that LN- patients with AOL low-risk may not be safely excluded from a 70-GS assessment, and additional risk factors should be considered in clinical decision-making.

NPI had been confirmed and validated to stratify the prognosis of BC by incorporating of three prognostic factors: nodal status, tumor size and histological grade[43]. Compared with previously established classical risk models, we found that the risk stratification obtained by NPI model was most discordant with the 70-GS risk outcomes. For instance, the majority of 70-GS high-risk LN + patients (67, 91.8%) were moderate prognosis evaluated by NPI, while only 3 (4.1%) LN + patients classified as poor prognosis by NPI model were 70-GS high risk patients, which indicated that LN + patients classified as having a moderate prognosis by the NPI model may not be suitable candidates for exemption from adjuvant chemotherapy. Meanwhile, as for LN- patients, the NPI model did not exhibit the statistical difference in predicted prognosis between 70-GS high and low risk (p = 0.075) patients. In our study, we observed no statistically different number of positive nodes, tumor volume and histological grade between the 70-GS low and high risk both for LN + and LN- patients (Table 1). Additionally, the majority of patients included in our study had early-stage breast cancer, with a higher proportion of pT1 patients compared to pT2 and pT3 patients. Hence, it was hypothesized that the NPI may not be a dependable tool for predicting the 70-GS risk, though larger cohort was warranted in the future. Although NPI was considered as a robust and globally recognized system for stratifying EBC risk, a systematic review uncovered significant heterogeneity in studies examining the relationship between NPI categories and actual 5- and 10-year survival rates [44]. Additionally, previous studies have indicated that the predictive capacity of the NPI was less effective compared to other alternative prognostic tools [45, 46]. As observed in previous researches, NPI was a sub-optimal tool in predicting 10-year overall survival (OS) and disease-free survival (DFS) [47]. More valuable risk factors should be included in the NPI model to improve its predictive ability[48].

Our study was the first to compare the consistency between the MonarchE (FDA/NMPA) and 70-GS risk. Notably, MonarchE and NATALEE were not traditional risk calculation models. MonarchE was a phase III clinical trial which was designed to identify the efficiency of abemaciclib combined with endocrine therapy for adjuvant treatment of high-risk patients with HR+/HER2- EBC patients[49]. NATALEE was a phase III trial to evaluate the efficacy of reboxilide combined with endocrine therapy (ET) in patients with HR+/HER2- EBC at risk of recurrence[12]. Both abemaciclib in MonarchE and ribociclib in NATALEE were cyclin dependent kinase (CDK) 4/6 inhibitors that interrupted the proliferation of malignant cells through inhibiting the progression in cell cycle[50]. Based on the results from MonarchE, FDA and NMPA approved different indications for the application of abemaciclib in EBC patients. The main difference is that patients with a high level of Ki67 (≥ 20%) in the indications were also classified as high-risk patients and should receive adjuvant therapy in NMPA. As for LN + patients, our analysis revealed no statistically significant difference in the proportion of high-risk individuals based on the MonarchE (FDA) criterion between those classified as high and low risk by the 70-GS score (p = 0.066). However, we observed a significantly higher percentage of high-risk patients (66.2% vs 33.8%, p < 0.001) identified by the MonarchE (NMPA) criterion within the 70-GS high-risk group, and a greater proportion of low-risk patients (77.6% vs 22.4%, p < 0.001) identified by the MonarchE (NMPA) criterion within the 70-GS low-risk group. This may be attributed to the fact that all patients analyzed in our study were from China. The incorporation of a high Ki67 level (≥ 20%) as a marker of high risk in LN + individuals notably improved the consistency between the MonarchE (NMPA) and 70-GS risk stratification models. As for LN- patients, the high-risk stratification determined by the NATALEE criterion failed to demonstrate perfect concordance with the 70-GS high-risk stratification (p = 0.056). Our analysis suggested that Chinese LN + EBC patients classified as high risk by MonarchE (NMPA) should undergo 70-GS testing, with a 66.2% likelihood of falling into the 70-GS high-risk group. Conversely, those classified as low-risk by MonarchE (NMPA) may be confidently excluded from 70-GS testing, as there was a 77.6% probability of belonging to the 70-GS low-risk group.

To figure out the common significant variables constructing our nomogram models, we took the intersection of the variables included in our previous nomogram model [19], LN + nomogram model and LN- nomogram model (Fig. 6). Through this process, PR positivity and Ki67 were identified as significant variables. Similarly, we all excluded ER from the final nomogram models as all the patients exhibited high expression level of ER while PR positivity (%) varied between 0% and 100%. In accordance with prior studies, our nomogram demonstrated a negative correlation between PR positivity (%) and high-risk 70-GS. [51, 52]. Low or absent PR expression may serve as a potential indicator for patients who could derive greater benefit from adjuvant chemotherapy[53]. The Ki67 index, a well-established proliferation marker, has consistently shown a negative correlation with breast cancer survival. Therefore, the elevated Ki67 index may indicate increased 70-GS risk and a higher probability of benefiting from chemotherapy[54, 55]. Further endeavors were crucial to enhance the inadequate interlaboratory reproducibility and reconcile the discordance in cutoff selection for this biomarker[40]. For example, oncologists might be more confident in omitting adjuvant chemotherapy for Luminal A patients utilizing simplified nomogram models and more likely rely on 21-gene RS or 70-GS signature to decide the treatment decisions for Luminal B patients[28], and the lower cutoff of Ki67 would categorize more patients into Luminal B subtype.

There were also some limitations in our research. First, this was a single-institution and the external validation was absent. Second, selection bias was inevitable in this retrospective trail. Third, though we had incorporated in as many parameters as possible, some important parameters such as PET/CT or MRI were ignored due to the incomplete clinical information. Lastly, though our nomogram models achieved perfect diagnostic performance, other methods such as decision tree model, artificial intelligence might also work and further validation was warranted.

We update and construct the novel user-friendly nomogram models to predict the high and low risk classification of 70-GS test in LN + and LN- patients and demonstrated enhanced prediction accuracy in distinguishing high and low 70-GS risk categories. We provide evidence that the status of lymph nodes should be taken into consideration when making clinical decision for individuals without access to the 70-GS testing.

Ethics approval and consent to participate

All procedures performed in our studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. This article does not contain any studies with animals performed by any of the authors. For this type of study, formal consent is not required. This retrospective study was approved by the Ethics Committee of the Peking Union Medical College (PUMC) Hospital, Chinese Academy of Medical Sciences

Funding

This work was supported by the Natural Science Foundation of China (No. 81001183) , the Chinese Academy of Medical Sciences (CAMS) Innovation Fund for Medical Sciences (CIFMS) 2021-I2M-1-014, National High Level Hospital Clinical Research Funding (Grant No. 2022-PUMCH-A-165 and 2022-PUMCH-B-039).

Competing interests

The authors have declared that no competing interests.

Availability of data and materials

The datasets analyzed during the current study are available from the corresponding author on reasonable request.

Author Contributions

Jie Lian, Ying Xu, Ru Yao: research idea generation, study design, data collection and analysis and manuscript writing. Fangyuan Chen, Jiahui Zhang, Yang Qu, Lu Gao and Yanna Zhang: data collection and patients' follow-up. Yanna Zhang and Songjie Shen: methodology and model development, patients’ treatment and data collection. Qingli Zhu: ultrasound relevant data collection writing-review and editing. Xinyu Ren: pathology relevant data collection and manuscript editing. Lingyan Kong: mammography relevant data collection and writing-review.

Bo Pan, Yidong Zhou and Qiang Sun: Project administration, supervision, and writing-review.

Data Availability

The datasets analyzed during the current study are available from the corresponding author on reasonable request.

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Prediction of the 70-gene signature (MammaPrint) high versus low risk by nomograms among axillary lymph node positive (LN+) and negative (LN-) Chinese breast cancer patients, a retrospective study

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Abstract

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Introduction

Patients and Methods

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Patient Population

Prediction of 70-GS Binary Risk with Nomogram Models Among LN + and LN- Patients’ Groups

Statistical analysis

Results

Prediction of 70-GS Binary Risk with Nomogram Models in LN + and LN- Patient Groups

Prediction of 70-GS binary risk with nomogram models without separating LN + and LN- patients groups

Discussion

Conclusion

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1