Analysis of Prognostic Factors Affecting the Brain Metastases Free Survival and Survival After Brain Metastases in Breast Cancer

Non-invasive Fluorescence Imaging of Breast Cancer Metastasis to the Brain in an Orthotopic Nude-mouse Model With Very-narrow-band-width Laser Excitation of Red Fluorescent Protein Resulting in an Ultra-bright Signal Without Skin Autofluorescence

BACKGROUND/AIM

Breast-cancer metastasis to the brain is an intractable disease. To discover improved therapy for this disease, we developed a precise non-invasively-imageable orthotopic nude-mouse model, using very-narrow-band-width laser fluorescence excitation.

MATERIALS AND METHODS

Female nu/nu nude mice, aged 4-8 weeks, were inoculated through the midline of the skull with triple-negative human MDA-MB-231 breast cancer cells (5×105) expressing red fluorescent protein (RFP). The mice were imaged with the Analytik Jena UVP Biospectrum Advanced at 520 nm excitation with peak emission at 605 nm.

RESULTS

Three weeks after injection of MDA-MB-231-RFP cells in the brain, non-invasive fluorescence images of the breast tumor growing on the brain were obtained. The images of the tumor were very bright, with well-defined margins with no detectable skin autofluorescence background. Images obtained at various angles showed that the extent of the tumor margins could be precisely measured. A skin flap over the skull confirmed that the tumor was growing on the surface of the brain which is a frequent occurrence in breast cancer.

CONCLUSION

A precise orthotopic model of RFP-expressing breast-cancer metastasis to the brain was developed that could be non-invasively imaged with very-narrow-band-width laser excitation, resulting in an ultra-bright, ultra-low-background signal. The model will be useful in discovering improved therapeutics for this recalcitrant disease.

Harnessing immunotherapy for brain metastases: insights into tumor-brain microenvironment interactions and emerging treatment modalities

Brain metastases signify a deleterious milestone in the progression of several advanced cancers, predominantly originating from lung, breast and melanoma malignancies, with a median survival timeframe nearing six months. Existing therapeutic regimens yield suboptimal outcomes; however, burgeoning insights into the tumor microenvironment, particularly the immunosuppressive milieu engendered by tumor-brain interplay, posit immunotherapy as a promising avenue for ameliorating brain metastases. In this review, we meticulously delineate the research advancements concerning the microenvironment of brain metastases, striving to elucidate the panorama of their onset and evolution. We encapsulate three emergent immunotherapeutic strategies, namely immune checkpoint inhibition, chimeric antigen receptor (CAR) T cell transplantation and glial cell-targeted immunoenhancement. We underscore the imperative of aligning immunotherapy development with in-depth understanding of the tumor microenvironment and engendering innovative delivery platforms. Moreover, the integration with established or avant-garde physical methodologies and localized applications warrants consideration in the prevailing therapeutic schema.

A Population-Based Research Utilized a Risk Stratification Model to Forecast the Overall Survival of Young Women With Diagnosed Stage IV Breast Cancer

BACKGROUND

The goal of this study is to develop a risk prediction model for estimating overall survival (OS) in young females diagnosed with stage IV breast cancer.

METHODS

The clinical information was retrieved from the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2015. To identify the dependent risk factors, we utilized the Cox proportional hazards regression model in both single and multivariate analyses. We then created a new nomogram to predict the 1-, 3-, and 5-year overall survival probability for these patients based on the identified risk factors.

RESULTS

Six hundred seventy-six patients who met the eligibility requirements were stochastically partitioned into training (n = 475) and validation (n = 201) groups in a 7:3 ratio. Histology, breast subtype, T classification, brain metastasis, bone metastasis, liver metastasis, and surgery were identified as independent prognostic factors for cancer. To predict the 1-, 3-, and 5-year overall survival (OS) probabilities, all of these independent factors were incorporated into nomograms. Our nomogram demonstrated a favorable discriminatory power, as evidenced by a C-index of 0.737 (95% CI: 0.708-0.766) and 0.717 (95% CI: 0.664-0.770) for the training and validation cohorts, respectively. The calibration curves showed satisfactory consistency in both cohorts. Using this nomogram, we developed a risk stratification model that categorized patients into low-, intermediate-, and high-risk groups.

CONCLUSION

The prediction model was more precisely to predict the OS of young females with stage IV breast cancer and could enable individualized risk estimation that could be conducive to physicians exploring therapeutic strategies for effectiveness.

A Radiomics-Based Model for Potentially More Accurate Identification of Subtypes of Breast Cancer Brain Metastases

PURPOSE

Breast cancer brain metastases (BCBM) may involve subtypes that differ from the primary breast cancer lesion. This study aimed to develop a radiomics-based model that utilizes preoperative brain MRI for multiclass classification of BCBM subtypes and to investigate whether the model offers better prediction accuracy than the assumption that primary lesions and their BCBMs would be of the same subtype (non-conversion model) in an external validation set.

MATERIALS AND METHODS

The training and external validation sets each comprised 51 cases (102 cases total). Four machine learning classifiers combined with three feature selection methods were trained on radiomic features and primary lesion subtypes for prediction of the following four subtypes: 1) hormone receptor (HR)+/human epidermal growth factor receptor 2 (HER2)-, 2) HR+/HER2+, 3) HR-/HER2+, and 4) triple-negative. After training, the performance of the radiomics-based model was compared to that of the non-conversion model in an external validation set using accuracy and F1-macro scores.

RESULTS

The rate of discrepant subtypes between primary lesions and their respective BCBMs were 25.5% (n=13 of 51) in the training set and 23.5% (n=12 of 51) in the external validation set. In the external validation set, the accuracy and F1-macro score of the radiomics-based model were significantly higher than those of the non-conversion model (0.902 vs. 0.765, p=0.004; 0.861 vs. 0.699, p=0.002).

CONCLUSION

Our radiomics-based model represents an incremental advance in the classification of BCBM subtypes, thereby facilitating a more appropriate personalized therapy.

Radiomic Signatures for Predicting Receptor Status in Breast Cancer Brain Metastases

Backgrounds

A significant proportion of breast cancer patients showed receptor discordance between primary cancers and breast cancer brain metastases (BCBM), which significantly affected therapeutic decision-making. But it was not always feasible to obtain BCBM tissues. The aim of the present study was to analyze the receptor status of primary breast cancer and matched brain metastases and establish radiomic signatures to predict the receptor status of BCBM.

Methods

The receptor status of 80 matched primary breast cancers and resected brain metastases were retrospectively analyzed. Radiomic features were extracted using preoperative brain MRI (contrast-enhanced T1-weighted imaging, T2-weighted imaging, T2 fluid-attenuated inversion recovery, and combinations of these sequences) collected from 68 patients (45 and 23 for training and test sets, respectively) with BCBM excision. Using least absolute shrinkage selection operator and logistic regression model, the machine learning-based radiomic signatures were constructed to predict the estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) status of BCBM.

Results

Discordance between the primary cancer and BCBM was found in 51.3% of patients, with 27.5%, 27.5%, and 5.0% discordance for ER, PR, and HER2, respectively. Loss of receptor expression was more common (33.8%) than gain (18.8%). The radiomic signatures built using combination sequences had the best performance in the training and test sets. The combination model yielded AUCs of 0.89, 0.88, and 0.87, classification sensitivities of 71.4%, 90%, and 87.5%, specificities of 81.2%, 76.9%, and 71.4%, and accuracies of 78.3%, 82.6%, and 82.6% for ER, PR, and HER2, respectively, in the test set.

Conclusions

Receptor conversion in BCBM was common, and radiomic signatures show potential for noninvasively predicting BCBM receptor status.

Incidence, risk factors and survival of patients with brain metastases at initial metastatic breast cancer diagnosis in China

Purpose

To characterize the incidence, risk factors and survival of patients with brain metastases at initial diagnosis of metastatic breast cancer (MBC) in China.

Methods

The China National Cancer Center database was used to identify 2087 MBC patients diagnosed between 2003 and 2015. Clinicopathological features, treatment and survival information were extracted. Multivariable logistic and Cox regression were performed to determine factors predictive of brain metastases at MBC diagnosis and survival, respectively.

Results

Brain metastases occurred in ninety patients (4.3%) at MBC diagnosis, and in 27 patients (2.5%), 42 patients (7.2%) and 21 patients (5.2%) with hormone receptor positive, human epidermal growth factor receptor 2 negative (HR + HER2-), HER2-positive and triple negative breast cancer (TNBC), respectively. HER2-positive subtype (OR = 2.38; 95% CI 1.40–4.04; p < 0.0001), TNBC subtype (OR = 1.89; 95% CI 1.02–3.51; p = 0.005), and metastases to all three sites of bone, liver and lungs (OR = 3.23; 95% CI 1.52–6.87; p = 0.002) were shown to increase the risk of BM at MBC diagnosis. Median survival after BM was 23.7 months. First-line tyrosine kinase inhibitors (TKI) improved survival compared to trastuzumab-based regimen (44.9 vs 35.4 months, p = 0.09). Factors that independently decreased BM death risk were ECOG<2, brain metastases only and multidisciplinary treatment.

Conclusion

HER2-positive and TNBC subtypes have a higher incidence of BM at initial MBC diagnosis. Brain screening might be considered in patients with HER2-positive disease at MBC diagnosis, and further prospective randomized study is warranted.

•

Large retrospective analysis focusing on patients with BM at initial MBC diagnosis.

•

HER2-positive subtype presented with the highest incidence of BM at initial MBC diagnosis.

•

Patients with brain metastases only and receiving multidisciplinary treatment have a superior OS.

•

Brain screening might be considered in HER2-positive patients with BM at MBC diagnosis.