Comparisons of clinical characteristics, prognosis, epidemiological factors, and genetic susceptibility between HER2-low and HER2-zero breast cancer among Chinese females

A Comprehensive Model Outperformed the Single Radiomics Model in Noninvasively Predicting the HER2 Status in Patients with Breast Cancer

RATIONALE AND OBJECTIVES

This study aimed to develop predictive models based on conventional magnetic resonance imaging (cMRI) and radiomics features for predicting human epidermal growth factor receptor 2 (HER2) status of breast cancer (BC) and compare their performance.

MATERIALS AND METHODS

A total of 287 patients with invasive BC in our hospital were retrospectively analyzed. All patients underwent preoperative breast MRI consisting of fat-suppressed T2-weighted imaging, axial dynamic contrast-enhanced MRI, and diffusion-weighted imaging sequences. From these sequences, radiomics features were derived. Three distinct models were established utilizing cMRI features, radiomics features, and a comprehensive model that amalgamated both. The predictive capabilities of these models were assessed using the receiver operating characteristic curve analysis. The comparative performance was then determined through the DeLong test and net reclassification improvement (NRI).

RESULTS

In a randomized split, the 287 patients with BC were allotted to either training (234; 46 HER2-zero, 107 HER2-low, 81 HER2-positive) or test (53; 8 HER2-zero, 27 HER2-low, 18 HER2-positive) at an 8:2 ratio. The mean area under the curve (AUCs) for cMRI, radiomics, and comprehensive models predicting HER2 status were 0.705, 0.819, and 0.859 in training set and 0.639, 0.797, and 0.842 in test set, respectively. DeLong's test indicated that the combined model's AUC surpassed the radiomics model significantly (p < 0.05). NRI analysis verified superiority of the combined model over the radiomics for BC HER2 prediction (NRI 25.0) in the test set.

CONCLUSION

The comprehensive model based on the combination of cMRI and radiomics features outperformed the single radiomics model in noninvasively predicting the three-tiered HER2 status in patients with BC.

Preoperative Differentiation of HER2-Zero and HER2-Low from HER2-Positive Invasive Ductal Breast Cancers Using BI-RADS MRI Features and Machine Learning Modeling

BACKGROUND

Accurate determination of human epidermal growth factor receptor 2 (HER2) is important for choosing optimal HER2 targeting treatment strategies. HER2-low is currently considered HER2-negative, but patients may be eligible to receive new anti-HER2 drug conjugates.

PURPOSE

To use breast MRI BI-RADS features for classifying three HER2 levels, first to distinguish HER2-zero from HER2-low/positive (Task-1), and then to distinguish HER2-low from HER2-positive (Task-2).

STUDY TYPE

Retrospective.

POPULATION

621 invasive ductal cancer, 245 HER2-zero, 191 HER2-low, and 185 HER2-positive. For Task-1, 488 cases for training and 133 for testing. For Task-2, 294 cases for training and 82 for testing.

FIELD STRENGTH/SEQUENCE

3.0 T; 3D T1-weighted DCE, short time inversion recovery T2, and single-shot EPI DWI.

ASSESSMENT

Pathological information and BI-RADS features were compared. Random Forest was used to select MRI features, and then four machine learning (ML) algorithms: decision tree (DT), support vector machine (SVM), k-nearest neighbors (k-NN), and artificial neural nets (ANN), were applied to build models.

STATISTICAL TESTS

Chi-square test, one-way analysis of variance, and Kruskal-Wallis test were performed. The P values <0.05 were considered statistically significant. For ML models, the generated probability was used to construct the ROC curves.

RESULTS

Peritumoral edema, the presence of multiple lesions and non-mass enhancement (NME) showed significant differences. For distinguishing HER2-zero from non-zero (low + positive), multiple lesions, edema, margin, and tumor size were selected, and the k-NN model achieved the highest AUC of 0.86 in the training set and 0.79 in the testing set. For differentiating HER2-low from HER2-positive, multiple lesions, edema, and margin were selected, and the DT model achieved the highest AUC of 0.79 in the training set and 0.69 in the testing set.

DATA CONCLUSION

BI-RADS features read by radiologists from preoperative MRI can be analyzed using more sophisticated feature selection and ML algorithms to build models for the classification of HER2 status and identify HER2-low.

LEVEL OF EVIDENCE: 4

TECHNICAL EFFICACY

Stage 2.

Clinicopathological characteristics and value of HER2-low expression evolution in breast cancer receiving neoadjuvant chemotherapy

OBJECTIVE

The present study aimed to evaluate the clinicopathological characteristics and value of HER2-low expression evolution in breast cancer receiving neoadjuvant chemotherapy (NAC).

METHODS

Patients with HER2 negative breast cancer receiving NAC from January 2017 to December 2020 were enrolled in this study. The clinicopathological characteristics, response to NAC, evolution of HER2 and prognostic value were retrospectively analyzed.

RESULTS

410 patients were included. The proportion of HR positive disease in HER2-low cases was higher than in HER2-zero population (75.8 % vs. 65.8 %, P = 0.040). No statistical significant difference in pCR rate was observed between HER2-low and HER2-zero patients (33.8 % vs. 39.3 %, P = 0.290) when pCR was defined as ypTis/0ypN0. Exploratory analysis revealed that the pCR rate of HER2-low cases was significantly lower than HER2-zero patients in the entire population (19.8 % vs. 33.3 %, P = 0.004) and HR positive population (12.6 % vs. 29.9 %, P = 0.001) when pCR was defined as ypT0ypN0. The evolution rate of HER2 expression after NAC was 31.0 % in HER2-zero patients and 24.7 % in HER2-low patients. Compared with patients with HR positive disease, patients with TNBC had higher evolution rate of HER2 expression after NAC (37.7 % vs. 23.6 %). Significant association was observed between HER2 evolution with histology type and Ki-67 index in HER2-zero patients and with lymph node involvement, HR status and Ki-67 index in HER2-low patients. Prognostic impact of HER2 evolution was not observed.

CONCLUSIONS

HR positive and HR negative HER2-low breast cancer exhibit different clinicopathological features, response to NAC and HER2 evolution after treatment.

Prognostic Impact of HER2-Low and HER2-Zero in Resectable Breast Cancer with Different Hormone Receptor Status: A Landmark Analysis of Real-World Data from the National Cancer Center of China

BACKGROUND

The prognostic impact of HER2-low on overall survival (OS) and disease-free survival (DFS) in patients with resectable breast cancer (BC) remains controversial, partly resulting from the hormone receptor (HR) status.

OBJECTIVE

To investigate the prognostic impact of HER2-low in different HR subgroups.

PATIENTS AND METHODS

We retrospectively retrieved medical records of treatment-naive primary HER2-low and HER2-zero BC patients who were diagnosed with invasive ductal carcinoma and underwent surgery in the Cancer Hospital of the Chinese Academy of Medical Sciences from January 2009 to September 2017 (n = 7371). We compared the clinicopathologic features and performed Cox regression and landmark survival analyses to explore the prognostic impact of HER2-low on survival outcomes during distinct post-surgery intervals-36 months, 60 months, and 120 months.

RESULTS

HER2-low BC, compared to HER2-zero BC, exhibited less aggressive clinicopathologic features, such as smaller invasion size, lower grade, increased nerve invasion, higher HR positivity, and a higher proportion of low-Ki67 cases. In the HR-positive subgroup, HER2-low demonstrated improved OS (p = 0.046) and DFS (p = 0.026) within 60 months. Conversely, HER2-low displayed worse DFS (p = 0.046) in the HR-negative subgroup after 36 months from surgery. The findings remained robust in uni- and multi-variable Cox models.

CONCLUSIONS

HER2-low BCs manifested less aggressive clinicopathologic features than the HER2-zero cases. The prognostic impact of HER2-low in resectable BCs exhibits variability contingent upon the patients' HR status.