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Subtypes of breast cancer show different spatial distributions of brain metastases.

ABSTRACT: The aim of our study was to test the hypothesis that the spatial distribution of breast cancer brain metastases (BM) differ according to their biological subtypes. MR images of 100 patients with BM from primary breast cancer were retrospectively reviewed. Patients were divided according to the biological subtype of the primary tumor, (triple-negative: 24, HER2 positive: 48, luminal: 28). All images marked with BMs were standardized to the human brain MRI atlas provided by the Montreal Neurological Institute 152 database. Distribution pattern of BM was evaluated with intra-group and intergroup analysis. In intra-group analysis, hot spots of metastases from triple-negative are evenly distributed in the brain, meanwhile BMs from HER2 positive and luminal type occur dominantly in occipital lobe and cerebellum. In intergroup analysis, BMs from triple-negative type occurred more often in frontal lobe, limbic region, and parietal lobe, compared with other types (P < .05). Breast cancer subtypes tend to demonstrate different spatial distributions of their BMs. These findings may have direct implications for dose modulation in prophylactic irradiation as well as for differential diagnoses. Thus, this result should be validated in future study with a larger population.

SUBMITTER: Kyeong S

PROVIDER: S-EPMC5695816 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Subtypes of breast cancer show different spatial distributions of brain metastases.

Kyeong Sunghyon S Cha Yoon Jin YJ Ahn Sung Gwe SG Suh Sang Hyun SH Son Eun Ju EJ Ahn Sung Jun SJ

PloS one 20171120 11

The aim of our study was to test the hypothesis that the spatial distribution of breast cancer brain metastases (BM) differ according to their biological subtypes. MR images of 100 patients with BM from primary breast cancer were retrospectively reviewed. Patients were divided according to the biological subtype of the primary tumor, (triple-negative: 24, HER2 positive: 48, luminal: 28). All images marked with BMs were standardized to the human brain MRI atlas provided by the Montreal Neurologic ...[more]

PMID: 29155879

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Project description:ImportanceApproximately 1 in 7 patients with metastatic breast cancer (MBC) will receive radiotherapy for brain metastases (BRM). Significant differences in cumulative incidence of BRM by breast cancer subtype may inform future BRM screening protocols.ObjectiveTo describe cumulative incidence of BRM among patients with de novo MBC.Design, setting, and participantsIn this population-based cohort study, population health administrative databases in Ontario, Canada, held at the ICES were used to identify patients diagnosed with de novo MBC between 2009 and 2018. Given that a code for BRM does not exist within ICES, we analyzed the incidence of radiotherapy for BRM. The median (IQR) follow-up was 19.3 (6.2-39.5) months. A total of 100 747 patients with a new diagnosis of breast cancer between January 2009 and December 2018 were identified. Of these patients, 17 955 were excluded because they had previous or subsequent malignant neoplasms, 583 were excluded because they were younger than 18 years, 974 were excluded because there was an invalid Ontario Health Insurance Plan number or a date of death on or before the index date. Among 81 235 remaining patients, 3916 were identified as having de novo MBC.ExposuresTreatment with radiotherapy for breast cancer BRM.Main outcomes and measuresCumulative incidence of radiotherapy for BRM accounting for the competing risk of death, and time from MBC diagnosis to brain radiotherapy. Kaplan-Meier analyses were performed for time-to-event end points. Logistic regression was used to account for confounding variables.ResultsAmong 3916 patients with MBC, 1215 (31.0%) had HR-positive/ERBB2 (formerly HER2)-negative cancer, 310 (7.9%) had ERBB2-positive/HR-positive cancer, 200 (5.1%) had ERBB2-positive/HR-negative cancer, 258 (6.6%) had TNBC, and the remaining 1933 patients (49.4%) had an unknown breast cancer subtype. The median (IQR) age at diagnosis was 63 (52-75). A total of 549 (14.0%) underwent stereotactic radiosurgery or whole brain radiotherapy for breast cancer BRM. Cumulative incidence of BRM was higher among patients with ERBB2-positive/HR-negative breast cancer (34.7%), ERBB2-positive/HR-positive breast cancer (28.1%), and triple-negative breast cancer (21.9%) compared to those with HR-positive/ERBB2-negative breast cancer (12.1%). The median (IQR) time from MBC diagnosis to brain radiotherapy ranged from 7.5 (2.3-17.4) months for patients with TNBC to 19.8 (12.2-35.1) months for those with ERBB2-positive/HR-positive breast cancer.Conclusions and relevanceIncidence and time to development of BRM vary significantly by breast cancer subtype. A better understanding of the biology of intracranial metastatic disease may help inform potential screening programs or preventative interventions.

Project description:BACKGROUND: Epidemiology and ecology share many fundamental research questions. Here we describe how principal coordinates of neighbor matrices (PCNM), a method from spatial ecology, can be applied to spatial epidemiology. PCNM is based on geographical distances among sites and can be applied to any set of sites providing a good coverage of a study area. In the present study, PCNM eigenvectors corresponding to positive autocorrelation were used as explanatory variables in linear regressions to model incidences of eight most common cancer types in Finnish municipalities (n?=?320). The dataset was provided by the Finnish Cancer Registry and it included altogether 615,839 cases between 1953 and 2010. RESULTS: PCNM resulted in 165 vectors with a positive eigenvalue. The first PCNM vector corresponded to the wavelength of hundreds of kilometers as it contrasted two main subareas so that municipalities located in southwestern Finland had the highest positive site scores and those located in midwestern Finland had the highest negative scores in that vector. Correspondingly, the 165(th) PCNM vector indicated variation mainly between the two small municipalities located in South Finland. The vectors explained 13?-?58% of the spatial variation in cancer incidences. The number of outliers having standardized residual > |3| was very low, one to six per model, and even lower, zero to two per model, according to Chauvenet's criterion. The spatial variation of prostate cancer was best captured (adjusted r (2)?=?0.579). CONCLUSIONS: PCNM can act as a complementary method to causal modeling to achieve a better understanding of the spatial structure of both the response and explanatory variables, and to assess the spatial importance of unmeasured explanatory factors. PCNM vectors can be used as proxies for demographics and causative agents to deal with autocorrelation, multicollinearity, and confounding variables. PCNM may help to extend spatial epidemiology to areas with limited availability of registers, improve cost-effectiveness, and aid in identifying unknown causative agents, and predict future trends in disease distributions and incidences. A large advantage of using PCNM is that it can create statistically valid reflectors of real predictors for disease incidence models with only little resources and background information.

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Subtypes of breast cancer show different spatial distributions of brain metastases.

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Subtypes of breast cancer show different spatial distributions of brain metastases.

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