Treatment response as predictor for brain metastasis in triple negative breast cancer: A score-based model

Development and external validation of a prediction model for brain metastases in patients with metastatic breast cancer

BACKGROUND

Breast cancer patients with brain metastasis (BM) have a poor prognosis. This study aims to identify the risk factors of BM in patients with metastatic breast cancer (MBC) and establish a competing risk model for predicting the risk of brain metastases at different time points along the course of disease.

METHODS

Patients with MBC admitted to the breast disease center of Peking University First Hospital from 2008 to 2019 were selected and retrospectively analyzed to establish a risk prediction model for brain metastases. Patients with MBC admitted to eight breast disease centers from 2015 to 2017 were selected for external validation of the competing risk model. The competing risk approach was used to estimate cumulative incidence. Univariate Fine-Gray competing risk regression, optimal subset regression, and LASSO Cox regression were used to screen potential predictors of brain metastases. Based on the results, a competing risk model for predicting brain metastases was established. The discrimination of the model was evaluated using AUC, Brier score, and C-index. The calibration was evaluated by the calibration curves. The model was assessed for clinical utility by decision curve analysis (DCA), as well as by comparing the cumulative incidence of brain metastases between groups with different predicted risks.

RESULTS

From 2008 to 2019, a total of 327 patients with MBC in the breast disease center of Peking University First Hospital were admitted into the training set for this study. Among them, 74 (22.6%) patients developed brain metastases. From 2015 to 2017, a total of 160 patients with MBC in eight breast disease centers were admitted into the validation set for this study. Among them, 26 (16.3%) patients developed brain metastases. BMI, age, histological type, breast cancer subtype, and extracranial metastasis pattern were included in the final competing risk model for BM. The C-index of the prediction model in the validation set was 0.695, and the AUCs for predicting the risk of brain metastases within 1, 3, and 5 years were 0.674, 0.670, and 0.729, respectively. Time-dependent DCA curves demonstrated a net benefit of the prediction model with thresholds of 9-26% and 13-40% when predicting the risk of brain metastases at 1 and 3 years, respectively. Significant differences were observed in the cumulative incidence of brain metastases between groups with different predicted risks (P < 0.05 by Gray's test).

CONCLUSIONS

In this study, a competing risk model for BM was innovatively established, with the multicenter data being used as an independent external validation set to confirm the predictive efficiency and universality of the model. The C-index, calibration curves, and DCA of the prediction model indicated good discrimination, calibration, and clinical utility, respectively. Considering the high risk of death in patients with metastatic breast cancer, the competing risk model of this study is more accurate in predicting the risk of brain metastases compared with the traditional Logistic and Cox regression models.

A pre-operative MRI-based brain metastasis risk-prediction model for triple-negative breast cancer

BACKGROUND

Triple-negative breast cancer (TNBC) patients have a high 2-year post-operative incidence of brain metastasis (BM). Currently, there is no early prediction tool to predict the risk of BM in TNBC patients.

METHODS

Data of breast cancer patients, who had been scanned, resected, and pathologically diagnosed at a local hospital from May 2012 to June 2018 were collected. Primary and radiological secondary exclusion criteria were used to determine patients' eligibility for inclusion in the study. Data for the TNBC cohort included qualified 2-year post-operative follow-up information, BM status, and pre-operative MRI data. Age-based propensity score matching (PSM) was used to build a comparable study cohort. The tumor regions of interest were segmented and used for lattice radiomics feature extraction. The filtered and normalized lattice radiomics features were then trained with BM status using the random forest (RF), support vector machine (SVM), k-nearest neighbor, least absolute shrinkage and selection operator regression, naïve Bayesian, and neural network algorithms. The generated prediction models were evaluated using 10-fold cross verification, and the areas under the curve (AUCs), accuracy, sensitivity, and specificity were reported.

RESULTS

Data from 643 breast cancer patients were collected. Among these, 84 TNBC cases (comprising 42 pairs) were included in this study after primary exclusion, radiological secondary exclusion, and PSM. We extracted 3,854 lattice radiomics features from the pre-operative MRI. Of these, 2,480 were used for model training after filtration. The 10-fold verification results showed that the BM risk-prediction model, which was based on the normalized and filtered lattice radiomics features of collected cases trained by naïve Bayesian algorithm, had a high AUC (0.878), accuracy (0.786), specificity (81.0%), and sensitivity (76.2%).

CONCLUSIONS

The pre-operative MRI data of TNBC patients can be used to predict 2-year BM risk. This application could help to achieve better early stratification, BM screening, and the overall prognosis.

A risk stratification model for predicting brain metastasis and brain screening benefit in patients with metastatic triple-negative breast cancer

BACKGROUND

Patients with metastatic triple-negative breast cancer (mTNBC) frequently experience brain metastasis. This study aimed to identify prognostic factors and construct a nomogram for predicting brain metastasis possibility and brain screening benefit in mTNBC patients.

METHODS

Patients with mTNBC treated at our institution between January 2011 and December 2018 were retrospectively analyzed. Fine and Gray's competing risks model was used to identify independent prognostic factors. By integrating these prognostic factors, a competing risk nomogram and risk stratification model were developed and evaluated with concordance index (C-index) and calibration curves.

RESULTS

A total of 472 patients were retrospectively analyzed, including 305 patients in the training set, 78 patients in the validation set I and 89 patients in the validation set II. Four clinicopathological factors were identified as independent prognostic factors in the nomogram: lung metastasis, number of metastatic organ sites, hilar/mediastinal lymph node metastasis and KI-67 index. The C-indexes and calibration plots showed that the nomogram exhibited a sufficient level of discrimination. A risk stratification was further generated to divide all the patients into three prognostic groups. The cumulative incidence of brain metastasis at 18 months was 5.3% (95% confidence interval [CI], 2.5%-9.7%) for patients in the low-risk group, while 14.3% (95% CI, 9.3%-20.4%) for patients with intermediate risk and 34.3% (95% CI, 26.8%-41.9%) for patients with high risk. Routine brain MRI screening improved overall survival in high-risk group (HR 0.67, 95% CI 0.46-0.98, P = .039), but not in low-risk group (HR 0.93, 95% CI 0.57-1.49, P = .751) and intermediate-risk group (HR 0.83, 95% CI 0.55-1.27, P = .386).

CONCLUSIONS

We have developed a robust tool that is able to predict subsequent brain metastasis in mTNBC patients. Our model will allow selection of patients at high risk for brain metastasis who might benefit from routine bran MRI screening.

Evaluation of imaging findings and prognostic factors after whole-brain radiotherapy for carcinomatous meningitis from breast cancer: A retrospective analysis

This study aimed to evaluate the imaging findings and prognostic factors after whole-brain radiotherapy in patients with carcinomatous meningitis from breast cancer.A retrospective analysis of imaging data and prognostic factors was performed in patients treated with whole-brain radiotherapy or whole-brain/spine radiotherapy immediately after the first diagnosis of carcinomatous meningitis from breast cancer at our hospital from January 1, 2010 to December 31, 2018. Statistical significance was set at P < .05 (two-tailed).All patients (n = 31) were females with the mean age of 58.0 ± 11.0 years. The breast cancer subtypes were luminal (n = 14, 45.1%), human epidermal growth factor receptor 2 (HER2)-positive (n = 9, 29.0%), and triple-negative (n = 8, 26.0%) breast cancer. Brain metastasis and abnormal contrast enhancement in the sulci were observed in 21 (67.7%) and 24 (80.6%) patients, respectively. The median survival time after cancerous meningitis diagnosis was 62 (range, 6-657) days. Log-rank test showed significant differences in median survival time after cancerous meningitis diagnosis: 18.0 days for subjects treated with 30 Gy in < 10 fractions (n = 7) vs 78.5 days for subjects treated with 30 Gy in ≥10 fractions (n = 24) (P < .01) and 23.0 days for the triple-negative subtype vs 78.5 days for the other subtype (P < .01) groups. Univariate analysis using the Cox regression model showed significant differences in median survival time after cancerous meningitis diagnosis between the group treated with 30 Gy in <10 fractions and the group treated in ≥10 fractions (hazard ratio [HR] 0.08, 95% confidence interval [CI], 0.03-0.26; P < .01), and between the triple-negative subtype and the other subtypes (HR = 5.48; 95% CI, 1.88-16.0; P < .01) groups.Discontinuation of whole-brain radiotherapy and the presence of triple-negative breast cancer were indicators of poor prognosis.

Pericyte-secreted IGF2 promotes breast cancer brain metastasis formation

Brain metastases are life-threatening complications of triple-negative breast cancer, melanoma, and a few other tumor types. Poor outcome of cerebral secondary tumors largely depends on the microenvironment formed by cells of the neurovascular unit, among which pericytes are the least characterized. By using in vivo and in vitro techniques and human samples, here we show that pericytes play crucial role in the development of metastatic brain tumors by directly influencing key steps of the development of the disease. Brain pericytes had a prompt chemoattractant effect on breast cancer cells and established direct contacts with them. By secreting high amounts of extracellular matrix proteins, pericytes enhanced adhesion of both melanoma and triple-negative cancer cells, which might be particularly important in the exclusive perivascular growth of these tumor cells. In addition, pericytes secreted insulin-like growth factor 2 (IGF2), which had a very significant pro-proliferative effect on mammary carcinoma, but not on melanoma cells. By inhibiting IGF2 signaling using silencing or picropodophyllin (PPP), we could block the proliferation-increasing effect of pericytes on breast cancer cells. Administration of PPP (a blood-brain barrier-permeable substance) significantly decreased the size of brain tumors in mice inoculated with triple-negative breast cancer cells. Taken together, our results indicate that brain pericytes have significant pro-metastatic features, especially in breast cancer. Our study underlines the importance of targeting pericytes and the IGF axis as potential strategies in brain metastatic diseases.

Predictors of Neoadjuvant Chemotherapy Response in Breast Cancer: A Review

Neoadjuvant chemotherapy (NAC) largely increases operative chances and improves prognosis of the local advanced breast cancer patients. However, no specific means have been invented to predict the therapy responses of patients receiving NAC. Therefore, we focus on the alterations of tumor tissue-related microenvironments such as stromal tumor-infiltrating lymphocytes status, cyclin-dependent kinase expression, non-coding RNA transcription or other small molecular changes, in order to detect potentially predicted biomarkers which reflect the therapeutic efficacy of NAC in different subtypes of breast cancer. Further, possible mechanisms are also discussed to discover feasible treatment targets. Thus, these findings will be helpful to promote the prognosis of breast cancer patients who received NAC and summarized in this review.