Impact of pathological features of brain metastases in prognosis

Risk factors and prognostic factors of brain metastasis of triple-negative breast cancer: A single-center retrospective study

OBJECTIVE

This retrospective study is to explore the risk factors and prognostic factors of brain metastases of triple-negative breast cancer (TNBC) in a single center.

METHODS

Clinical data of patients with stages I-III TNBC were collected. The Kaplan-Meier method, log-rank test, and stepwise COX regression were performed.

RESULTS

The 437 patients with stages I-III TNBC were followed up for five years. Among them, 89 cases (20.4%) developed brain metastases, and they were followed up for 2 years after brain metastasis. The cumulative brain metastasis rates of TNBC patients at six months, one year, two years, three years, and five years were 1.38%, 5.75%, 12.94%, 17.63%, and 21.26%, respectively. Multivariate analysis suggested that the first diagnosis age ≤35 years old, advanced pathological stage, lymph node metastasis, and Ki-67 ≥30% represented the risk factors for brain metastasis. In contrast, the surgical method was a protective factor for brain metastasis. The median survival time after brain metastasis was 4.87 months. The survival rates at one, three, six, 12, and 24 months were 84.27%, 60.67%, 34.83%, 15.69%, and 6.64%, respectively. The age >60 years at first diagnosis, Ki-67 ≥30%, local recurrence, and distant metastasis were closely related to the poor prognosis of TNBC patients with brain metastases, while radiotherapy alone, systemic therapy, and combined chemotherapy and radiotherapy represented the prognostic protective factors.

CONCLUSIONS

Patient age, Ki-67 level, metastasis, and treatment methods are the risk factors and prognostic factors for brain metastasis of TNBC. Surgical resection of the primary lesion during the first treatment is essential to reduce the incidence of brain metastases. Close postoperative follow-up (such as brain magnetic resonance imaging [MRI]) within 2-3 years after surgery is recommended to improve the prognosis.

Evaluation of brain metastasis edema in breast cancer patients as a marker for Ki-67 and cell count-A single center analysis

BACKGROUND

Peritumoral edema is an important cause of morbidity and mortality in patients with breast cancer brain metastases (BCBM). The relationship between vasogenic edema and proliferation indices or cell density in BCBM remains poorly understood.

PURPOSE

To assess the association between tumor volume and peritumoral edema volume and histopathological and immunohistochemical parameters in BCBM.

MATERIALS AND METHODS

Patients with confirmed BCBM were retrospectively identified. The tumor volume and peritumoral edema volume of each brain metastasis (BM) were semi-automatically calculated in axial T2w and axial T2-fluid attenuated inversion recovery (FLAIR) sequences using the software MIM (Cleveland, Ohio, USA). Edema volume was correlated with histological parameters, including cell count and Ki-67. Sub-analyses were conducted for luminal B, Her2-positive, and tripe negative subgroups.

RESULTS

Thirty-eight patients were included in the study. There were 24 patients with a single BM. Mean metastasis volume was 31.40 ± 32.52 mL and mean perifocal edema volume was 72.75 ± 58.85 mL. In the overall cohort, no correlation was found between tumor volume and Ki-67 (r = 0.046, p = .782) or cellularity (r = 0.028, p = .877). Correlation between edema volume and Ki-67 was r = 0.002 (p = .989), correlation with cellularity was r = 0.137 (p = .453). No relevant correlation was identified in any subgroup analysis. There was no relevant correlation between BM volume and edema volume.

CONCLUSION

In patients with breast cancer brain metastases, we did not find linear associations between edema volumes and immunohistochemical features reflecting proliferation potential. Furthermore, there was no relevant correlation between metastasis volume and edema volume.

The relationship between connexin-43 expression and Ki67 in non-glial central nervous system tumors

BACKGROUND

Advanced intercellular communication is a known oncogenic factor. In the central nervous system, Connexin-43 (Cx43) forms this junctional networking. Moreover, it correlates with the proliferation rate, and thus behavior, of gliomas. We assessed the expression of Cx43 and its relationship to Ki67 in other common central nervous system tumors.

METHODS

The expression of Cx43 and Ki67 were assessed in formalin-fixed paraffin embedded samples of human brain metastases, meningiomas, and neurinomas using immunohistochemistry. Neurinomas and meningiomas were jointly evaluated due to similar non-malignant behavior.

RESULTS

A total of 14 metastases of different extracerebral carcinomas, 6 meningiomas, and 10 neurinomas were evaluated. Five (36%) metastases and 5 (31%) meningiomas/neurinomas showed minor expression, whereas 6 (43%) metastases and 2 (13%) meningiomas/neurinomas showed no Cx43 expression at all. In 3 (21%) metastases and 9 (56%) meningiomas/neurinomas, moderate or strong expression of Cx43 was identified. The higher expression of Cx43 in meningiomas and neurinomas directly correlated with Ki67, r = 0.53 (P = 0.034). For metastases no significant correlation was found. Mitotic index in meningiomas/neurinomas correlated with Ki67 expression, r = 0.74 (P < 0.001), but did not show statistically significant correlation with Cx43 expression in these tumors.

CONCLUSIONS

The expression of Cx43 as a marker of cell-to-cell networking exposed a significant correlation with the Ki67-defined proliferation index in case of primary central nervous system neuroectodermal neoplasms. However, it does not seem to play a comparable role in metastases with extracerebral origin.

An integrative non-invasive malignant brain tumors classification and Ki-67 labeling index prediction pipeline with radiomics approach

BACKGROUND

The histological sub-classes of brain tumors and the Ki-67 labeling index (LI) of tumor cells are major factors in the diagnosis, prognosis, and treatment management of patients. Many existing studies primarily focused on the classification of two classes of brain tumors and the Ki-67LI of gliomas. This study aimed to develop a preoperative non-invasive radiomics pipeline based on multiparametric-MRI to classify-three types of brain tumors, glioblastoma (GBM), metastasis (MET) and primary central nervous system lymphoma (PCNSL), and to predict their corresponding Ki-67LI.

METHODS

In this retrospective study, 153 patients with malignant brain tumors were involved. The radiomics features were extracted from three types of MRI (T1-weighted imaging (T1WI), fluid-attenuated inversion recovery (FLAIR), and contrast-enhanced T1-weighted imaging (CE-T1WI)) with three masks (tumor core, edema, and whole tumor masks) and selected by a combination of Pearson correlation coefficient (CORR), LASSO, and Max-Relevance and Min-Redundancy (mRMR) filters. The performance of six classifiers was compared and the top three performing classifiers were used to construct the ensemble learning model (ELM). The proposed ELM was evaluated in the training dataset (108 patients) by 5-fold cross-validation and in the test dataset (45 patients) by hold-out. The accuracy (ACC), sensitivity (SEN), specificity (SPE), F1-Score, and the area under the receiver operating characteristic curve (AUC) indicators evaluated the performance of the models.

RESULTS

The best feature sets and ELM with the optimal performance were selected to construct the tri-categorized brain tumor aided diagnosis model (training dataset AUC: 0.96 (95% CI: 0.93, 0.99); test dataset AUC: 0.93) and Ki-67LI prediction model (training dataset AUC: 0.96 (95% CI: 0.94, 0.98); test dataset AUC: 0.91). The CE-T1WI was the best single modality for all classifiers. Meanwhile, the whole tumor was the most vital mask for the tumor classification and the tumor core was the most vital mask for the Ki-67LI prediction.

CONCLUSION

The developed radiomics models led to the precise preoperative classification of GBM, MET, and PCNSL and the prediction of Ki-67LI, which could be utilized in clinical practice for the treatment planning for brain tumors.

Radiomics as an emerging tool in the management of brain metastases

Brain metastases (BM) are associated with significant morbidity and mortality in patients with advanced cancer. Despite significant advances in surgical, radiation, and systemic therapy in recent years, the median overall survival of patients with BM is less than 1 year. The acquisition of medical images, such as computed tomography (CT) and magnetic resonance imaging (MRI), is critical for the diagnosis and stratification of patients to appropriate treatments. Radiomic analyses have the potential to improve the standard of care for patients with BM by applying artificial intelligence (AI) with already acquired medical images to predict clinical outcomes and direct the personalized care of BM patients. Herein, we outline the existing literature applying radiomics for the clinical management of BM. This includes predicting patient response to radiotherapy and identifying radiation necrosis, performing virtual biopsies to predict tumor mutation status, and determining the cancer of origin in brain tumors identified via imaging. With further development, radiomics has the potential to aid in BM patient stratification while circumventing the need for invasive tissue sampling, particularly for patients not eligible for surgical resection.

Differentiation of brain metastases originating from lung and breast cancers using apparent diffusion coefficient histogram analysis and the relation of histogram parameters with Ki-67

PURPOSE

A fast, reliable and non-invasive method is required in differentiating brain metastases (BMs) originating from lung cancer (LC) and breast cancer (BC). The aims of this study were to assess the role of histogram analysis of apparent diffusion coefficient (ADC) maps in differentiating BMs originated from LC and BC, and then to investigate further the association of ADC histogram parameters with Ki-67 index in BMs.

METHODS

A total of 55 patients (LC, N = 40; BC, N = 15) with BMs histopathologically confirmed were enrolled in the study. The LC group was divided into small-cell lung cancer (SCLC; N = 15) and non-small-cell lung cancer (NSCLC; N = 25) groups. ADC histogram parameters (ADC_max, ADC_mean, ADC_min, ADC_median, ADC₁₀, ADC₂₅, ADC₇₅ and ADC₉₀, skewness, kurtosis and entropy) were derived from ADC maps. Mann-Whitney U-test, independent samples t-test, receiver operating characteristic (ROC) analysis and Spearman correlation analysis were used for statistical assessment.

RESULTS

ADC histogram parameters did not show significant differences between LC and BC groups (p > 0.05). Subgroup analysis showed that various ADC histogram parameters were found to be statistically lower in the SCLC group compared to the NSCLC and BC groups (p < 0.05). ROC analysis showed that ADC_mean and ADC₁₀ for differentiating SCLC BMs from NSCLC, and ADC₂₅ for differentiating SCLC BMs from BC achieved optimal diagnostic performances. Various histogram parameters were found to be significantly correlated with Ki-67 (p < 0.05).

CONCLUSION

Histogram analysis of ADC maps may reflect tumoural proliferation potential in BMs and can be useful in differentiating SCLC BMs from NSCLC and BC BMs.

Next-generation immunotherapies for brain metastatic cancers

Patients with extracranial tumors, like lung, breast, and skin cancers, often develop brain metastases (BM) during the course of their diseases and BM commonly represent the terminal stage of cancer progression. Recent insights in the immune biology of BM and the increasing focus of immunotherapy as a therapeutic option for cancer has prompted testing of promising biological immunotherapies, including immune cell-targeting, virotherapy, vaccines, and different cell-based therapies. Here, we review the pathobiology of BM progression and evaluate the potential of next-generation immunotherapies for BM tumors. We also provide future perspectives on the development and implementation of such therapies for brain metastatic cancer patients.

Surfactant Expression Defines an Inflamed Subtype of Lung Adenocarcinoma Brain Metastases that Correlates with Prolonged Survival

PURPOSE

To provide a better understanding of the interplay between the immune system and brain metastases to advance therapeutic options for this life-threatening disease.

EXPERIMENTAL DESIGN

Tumor-infiltrating lymphocytes (TIL) were quantified by semiautomated whole-slide analysis in brain metastases from 81 lung adenocarcinomas. Multi-color staining enabled phenotyping of TILs (CD3, CD8, and FOXP3) on a single-cell resolution. Molecular determinants of the extent of TILs in brain metastases were analyzed by transcriptomics in a subset of 63 patients. Findings in lung adenocarcinoma brain metastases were related to published multi-omic primary lung adenocarcinoma The Cancer Genome Atlas data (n = 230) and single-cell RNA-sequencing (scRNA-seq) data (n = 52,698).

RESULTS

TIL numbers within tumor islands was an independent prognostic marker in patients with lung adenocarcinoma brain metastases. Comparative transcriptomics revealed that expression of three surfactant metabolism-related genes (SFTPA1, SFTPB, and NAPSA) was closely associated with TIL numbers. Their expression was not only prognostic in brain metastasis but also in primary lung adenocarcinoma. Correlation with scRNA-seq data revealed that brain metastases with high expression of surfactant genes might originate from tumor cells resembling alveolar type 2 cells. Methylome-based estimation of immune cell fractions in primary lung adenocarcinoma confirmed a positive association between lymphocyte infiltration and surfactant expression. Tumors with a high surfactant expression displayed a transcriptomic profile of an inflammatory microenvironment.

CONCLUSIONS

The expression of surfactant metabolism-related genes (SFTPA1, SFTPB, and NAPSA) defines an inflamed subtype of lung adenocarcinoma brain metastases characterized by high abundance of TILs in close vicinity to tumor cells, a prolonged survival, and a tumor microenvironment which might be more accessible to immunotherapeutic approaches.