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1.  Altered resting state functional brain network topology in chemotherapy-treated breast cancer survivors 
Neurobiology of disease  2012;48(3):329-338.
Many women with breast cancer, especially those treated with chemotherapy, experience cognitive decline due in part to neurotoxic brain injury. Recent neuroimaging studies suggest widespread brain structural abnormalities pointing to disruption of large-scale brain networks. We applied resting state functional magnetic resonance imaging and graph theoretical analysis to examine the connectome in breast cancer survivors treated with chemotherapy relative to healthy comparison women. Compared to healthy females, the breast cancer group displayed altered global brain network organization characterized by significantly decreased global clustering as well as disrupted regional network characteristics in frontal, striatal and temporal areas. Breast cancer survivors also showed significantly increased self-report of executive function and memory difficulties compared to healthy females. These results suggest that topological organization of both global and regional brain network properties may be disrupted following breast cancer and chemotherapy. This pattern of altered network organization is believed to result in reduced efficiency of parallel information transfer. This is the first report of alterations in large-scale functional brain networks in this population and contributes novel information regarding the neurobiologic mechanisms underlying breast cancer-related cognitive impairment.
PMCID: PMC3461109  PMID: 22820143
breast cancer; chemotherapy; cognition; brain; resting state fMRI; graph theory; connectome
2.  GAT: A Graph-Theoretical Analysis Toolbox for Analyzing Between-Group Differences in Large-Scale Structural and Functional Brain Networks 
PLoS ONE  2012;7(7):e40709.
In recent years, graph theoretical analyses of neuroimaging data have increased our understanding of the organization of large-scale structural and functional brain networks. However, tools for pipeline application of graph theory for analyzing topology of brain networks is still lacking. In this report, we describe the development of a graph-analysis toolbox (GAT) that facilitates analysis and comparison of structural and functional network brain networks. GAT provides a graphical user interface (GUI) that facilitates construction and analysis of brain networks, comparison of regional and global topological properties between networks, analysis of network hub and modules, and analysis of resilience of the networks to random failure and targeted attacks. Area under a curve (AUC) and functional data analyses (FDA), in conjunction with permutation testing, is employed for testing the differences in network topologies; analyses that are less sensitive to the thresholding process. We demonstrated the capabilities of GAT by investigating the differences in the organization of regional gray-matter correlation networks in survivors of acute lymphoblastic leukemia (ALL) and healthy matched Controls (CON). The results revealed an alteration in small-world characteristics of the brain networks in the ALL survivors; an observation that confirm our hypothesis suggesting widespread neurobiological injury in ALL survivors. Along with demonstration of the capabilities of the GAT, this is the first report of altered large-scale structural brain networks in ALL survivors.
PMCID: PMC3396592  PMID: 22808240
3.  DEAR1 Is a Dominant Regulator of Acinar Morphogenesis and an Independent Predictor of Local Recurrence-Free Survival in Early-Onset Breast Cancer 
PLoS Medicine  2009;6(5):e1000068.
Ann Killary and colleagues describe a new gene that is genetically altered in breast tumors, and that may provide a new breast cancer prognostic marker.
Breast cancer in young women tends to have a natural history of aggressive disease for which rates of recurrence are higher than in breast cancers detected later in life. Little is known about the genetic pathways that underlie early-onset breast cancer. Here we report the discovery of DEAR1 (ductal epithelium–associated RING Chromosome 1), a novel gene encoding a member of the TRIM (tripartite motif) subfamily of RING finger proteins, and provide evidence for its role as a dominant regulator of acinar morphogenesis in the mammary gland and as an independent predictor of local recurrence-free survival in early-onset breast cancer.
Methods and Findings
Suppression subtractive hybridization identified DEAR1 as a novel gene mapping to a region of high-frequency loss of heterozygosity (LOH) in a number of histologically diverse human cancers within Chromosome 1p35.1. In the breast epithelium, DEAR1 expression is limited to the ductal and glandular epithelium and is down-regulated in transition to ductal carcinoma in situ (DCIS), an early histologic stage in breast tumorigenesis. DEAR1 missense mutations and homozygous deletion (HD) were discovered in breast cancer cell lines and tumor samples. Introduction of the DEAR1 wild type and not the missense mutant alleles to complement a mutation in a breast cancer cell line, derived from a 36-year-old female with invasive breast cancer, initiated acinar morphogenesis in three-dimensional (3D) basement membrane culture and restored tissue architecture reminiscent of normal acinar structures in the mammary gland in vivo. Stable knockdown of DEAR1 in immortalized human mammary epithelial cells (HMECs) recapitulated the growth in 3D culture of breast cancer cell lines containing mutated DEAR1, in that shDEAR1 clones demonstrated disruption of tissue architecture, loss of apical basal polarity, diffuse apoptosis, and failure of lumen formation. Furthermore, immunohistochemical staining of a tissue microarray from a cohort of 123 young female breast cancer patients with a 20-year follow-up indicated that in early-onset breast cancer, DEAR1 expression serves as an independent predictor of local recurrence-free survival and correlates significantly with strong family history of breast cancer and the triple-negative phenotype (ER−, PR−, HER-2−) of breast cancers with poor prognosis.
Our data provide compelling evidence for the genetic alteration and loss of expression of DEAR1 in breast cancer, for the functional role of DEAR1 in the dominant regulation of acinar morphogenesis in 3D culture, and for the potential utility of an immunohistochemical assay for DEAR1 expression as an independent prognostic marker for stratification of early-onset disease.
Editors' Summary
Each year, more than one million women discover that they have breast cancer. This type of cancer begins when cells in the breast that line the milk-producing glands or the tubes that take the milk to the nipples (glandular and ductal epithelial cells, respectively) acquire genetic changes that allow them to grow uncontrollably and to move around the body (metastasize). The uncontrolled division leads to the formation of a lump that can be detected by mammography (a breast X-ray) or by manual breast examination. Breast cancer is treated by surgical removal of the lump or, if the cancer has started to spread, by removal of the whole breast (mastectomy). Surgery is usually followed by radiotherapy or chemotherapy. These “adjuvant” therapies are designed to kill any remaining cancer cells but can make patients very ill. Generally speaking, the outlook for women with breast cancer is good. In the US, for example, nearly 90% of affected women are still alive five years after their diagnosis.
Why Was This Study Done?
Although breast cancer is usually diagnosed in women in their 50s or 60s, some women develop breast cancer much earlier. In these women, the disease is often very aggressive. Compared to older women, young women with breast cancer have a lower overall survival rate and their cancer is more likely to recur locally or to metastasize. It would be useful to be able to recognize those younger women at the greatest risk of cancer recurrence so that they could be offered intensive surveillance and adjuvant therapy; those women at a lower risk could have gentler treatments. To achieve this type of “stratification,” the genetic changes that underlie breast cancer in young women need to be identified. In this study, the researchers discover a gene that is genetically altered (by mutations or deletion) in early-onset breast cancer and then investigate whether its expression can predict outcomes in women with this disease.
What Did the Researchers Do and Find?
The researchers used “suppression subtractive hybridization” to identify a new gene in a region of human Chromosome 1 where loss of heterozygosity (LOH; a genetic alteration associated with cancer development) frequently occurs. They called the gene DEAR1 (ductal epithelium-associated RING Chromosome 1) to indicate that it is expressed in ductal and glandular epithelial cells and encodes a “RING finger” protein (specifically, a subtype called a TRIM protein; RING finger proteins such as BRCA1 and BRCA2 have been implicated in early cancer development and in a large fraction of inherited breast cancers). DEAR1 expression was reduced or lost in several ductal carcinomas in situ (a local abnormality that can develop into breast cancer) and advanced breast cancers, the researchers report. Furthermore, many breast tumors carried DEAR1 missense mutations (genetic changes that interfere with the normal function of the DEAR1 protein) or had lost both copies of DEAR1 (the human genome contains two copies of most genes). To determine the function of DEAR1, the researchers replaced a normal copy of DEAR1 into a breast cancer cell that had a mutation in DEAR1. They then examined the growth of these genetically manipulated cells in special three-dimensional cultures. The breast cancer cells without DEAR1 grew rapidly without an organized structure while the breast cancer cells containing the introduced copy of DEAR1 formed structures that resembled normal breast acini (sac-like structures that secrete milk). In normal human mammary epithelial cells, the researchers silenced DEAR1 expression and also showed that without DEAR1, the normal mammary cells lost their ability to form proper acini. Finally, the researchers report that DEAR1 expression (detected “immunohistochemically”) was frequently lost in women who had had early-onset breast cancer and that the loss of DEAR1 expression correlated with reduced local recurrence-free survival, a strong family history of breast cancer and with a breast cancer subtype that has a poor outcome.
What Do These Findings Mean?
These findings indicate that genetic alteration and loss of expression of DEAR1 are common in breast cancer. Although laboratory experiments may not necessarily reflect what happens in people, the results from the three-dimensional culture of breast epithelial cells suggest that DEAR1 may regulate the normal acinar structure of the breast. Consequently, loss of DEAR1 expression could be an early event in breast cancer development. Most importantly, the correlation between DEAR1 expression and both local recurrence in early-onset breast cancer and a breast cancer subtype with a poor outcome suggests that it might be possible to use DEAR1 expression to identify women with early-onset breast cancer who have an increased risk of local recurrence so that they get the most appropriate treatment for their cancer.
Additional Information
Please access these Web sites via the online version of this summary at
This study is further discussed in a PLoS Medicine Perspective by Senthil Muthuswamy
The US National Cancer Institute provides detailed information for patients and health professionals on all aspects of breast cancer, including information on genetic alterations in breast cancer (in English and Spanish)
The MedlinePlus Encyclopedia provides information for patients about breast cancer; MedlinePlus also provides links to many other breast cancer resources (in English and Spanish)
The UK charities Cancerbackup (now merged with MacMillan Cancer Support) and Cancer Research UK also provide detailed information about breast cancer
PMCID: PMC2673042  PMID: 19536326
4.  Anomalous Gray Matter Structural Networks in Patients with Hepatitis B Virus-Related Cirrhosis without Overt Hepatic Encephalopathy 
PLoS ONE  2015;10(3):e0119339.
Background and Purpose
Increasing evidence suggests that cirrhosis may affect the connectivity among different brain regions in patients before overt hepatic encephalopathy (OHE) occurs. However, there has been no study investigating the structural reorganization of these altered connections at the network level. The primary focus of this study was to investigate the abnormal topological organization of the structural network in patients with hepatitis B virus-related cirrhosis (HBV-RC) without OHE using structural MRI.
Using graph theoretical analysis, we compared the global and regional topological properties of gray matter structural networks between 28 patients with HBV-RC without OHE and 30 age-, sex- and education-matched healthy controls. The structural correlation networks were constructed for the two groups based on measures of gray matter volume.
The brain network of the HBV-RC group exhibited a significant decrease in the clustering coefficient and reduced small-worldness at the global level across a range of network densities. Regionally, brain areas with altered nodal degree/betweenness centrality were observed predominantly in association cortices (frontal and temporal regions) (p < 0.05, uncorrected), including a significantly decreased nodal degree in the inferior temporal gyrus (p < 0.001, uncorrected). Furthermore, the HBV-RC group exhibited a loss of association hubs and the emergence of an increased number of non-association hubs compared with the healthy controls.
The results of this large-scale gray matter structural network study suggest reduced topological organization efficiency in patients with HBV-RC without OHE. Our findings provide new insight concerning the mechanisms of neurobiological reorganization in the HBV-RC brain from a network perspective.
PMCID: PMC4364769  PMID: 25786256
5.  A prospective study of grey matter and cognitive function alterations in chemotherapy-treated breast cancer patients 
SpringerPlus  2014;3:444.
Subsequent to chemotherapy treatment, breast cancer patients often report a decline in cognitive functioning that can adversely impact many aspects of their lives. Evidence has mounted in recent years indicating that a portion of breast cancer survivors who have undergone chemotherapy display reduced performance on objective measures of cognitive functioning relative to comparison groups. Neurophysiological support for chemotherapy-related cognitive impairment has been accumulating due to an increase in neuroimaging studies in this field; however, longitudinal studies are limited and have not examined the relationship between structural grey matter alterations and neuropsychological performance. The aim of this study was to extend the cancer-cognition literature by investigating the association between grey matter attenuation and objectively measured cognitive functioning in chemotherapy-treated breast cancer patients.
Female breast cancer patients (n = 19) underwent magnetic resonance imaging after surgery but before commencing chemotherapy, one month following treatment, and one year after treatment completion. Individually matched controls (n = 19) underwent imaging at similar intervals. All participants underwent a comprehensive neuropsychological battery comprising four cognitive domains at these same time points. Longitudinal grey matter changes were investigated using voxel-based morphometry.
One month following chemotherapy, patients had distributed grey matter volume reductions. One year after treatment, a partial recovery was observed with alterations persisting predominantly in frontal and temporal regions. This course was not observed in the healthy comparison group. Processing speed followed a similar trajectory within the patient group, with poorest scores obtained one month following treatment and some improvement evident one year post-treatment.
This study provides further credence to patient claims of altered cognitive functioning subsequent to chemotherapy treatment.
PMCID: PMC4149682  PMID: 25184110
Breast cancer; Voxel-based morphometry; Chemotherapy; Cognition; MRI; Neuroimaging
6.  Abnormal Cortical Networks in Mild Cognitive Impairment and Alzheimer's Disease 
PLoS Computational Biology  2010;6(11):e1001006.
Recently, many researchers have used graph theory to study the aberrant brain structures in Alzheimer's disease (AD) and have made great progress. However, the characteristics of the cortical network in Mild Cognitive Impairment (MCI) are still largely unexplored. In this study, the gray matter volumes obtained from magnetic resonance imaging (MRI) for all brain regions except the cerebellum were parcellated into 90 areas using the automated anatomical labeling (AAL) template to construct cortical networks for 98 normal controls (NCs), 113 MCIs and 91 ADs. The measurements of the network properties were calculated for each of the three groups respectively. We found that all three cortical networks exhibited small-world properties and those strong interhemispheric correlations existed between bilaterally homologous regions. Among the three cortical networks, we found the greatest clustering coefficient and the longest absolute path length in AD, which might indicate that the organization of the cortical network was the least optimal in AD. The small-world measures of the MCI network exhibited intermediate values. This finding is logical given that MCI is considered to be the transitional stage between normal aging and AD. Out of all the between-group differences in the clustering coefficient and absolute path length, only the differences between the AD and normal control groups were statistically significant. Compared with the normal controls, the MCI and AD groups retained their hub regions in the frontal lobe but showed a loss of hub regions in the temporal lobe. In addition, altered interregional correlations were detected in the parahippocampus gyrus, medial temporal lobe, cingulum, fusiform, medial frontal lobe, and orbital frontal gyrus in groups with MCI and AD. Similar to previous studies of functional connectivity, we also revealed increased interregional correlations within the local brain lobes and disrupted long distance interregional correlations in groups with MCI and AD.
Author Summary
Understanding the progression of Alzheimer's disease (AD) is essential. We investigated networks of cortical connectivity along a continuum from normal to AD. Mild Cognitive Impairment (MCI) has been implicated as transitional between normal aging and AD. By investigating the characteristics of cortical networks in these three stages (normal, MCI and AD), we found that all three networks exhibited small-world properties. These properties indicate efficient information transfer in the human brain. We also found that the small-world measures of the MCI network were intermediate to those of the normal controls and the patients with AD. This supports the opinion that MCI is a transitional stage between normal aging and AD. Additionally, we found altered interregional correlations in patients with MCI and AD, which may indicate that a compensatory system interacts with cerebral atrophy. The presence of compensatory mechanisms in patients with MCI and AD may enable them to use additional cognitive resources to function on a more nearly normal level. In future, we need to integrate the multi-level network features obtained with various functional and anatomical brain imaging technologies on different scales to understand the pathophysiological mechanism of MCI and AD. We propose brainnetome to represent such integration framework.
PMCID: PMC2987916  PMID: 21124954
7.  Influence of Choice of Null Network on Small-World Parameters of Structural Correlation Networks 
PLoS ONE  2013;8(6):e67354.
In recent years, coordinated variations in brain morphology (e.g., volume, thickness) have been employed as a measure of structural association between brain regions to infer large-scale structural correlation networks. Recent evidence suggests that brain networks constructed in this manner are inherently more clustered than random networks of the same size and degree. Thus, null networks constructed by randomizing topology are not a good choice for benchmarking small-world parameters of these networks. In the present report, we investigated the influence of choice of null networks on small-world parameters of gray matter correlation networks in healthy individuals and survivors of acute lymphoblastic leukemia. Three types of null networks were studied: 1) networks constructed by topology randomization (TOP), 2) networks matched to the distributional properties of the observed covariance matrix (HQS), and 3) networks generated from correlation of randomized input data (COR). The results revealed that the choice of null network not only influences the estimated small-world parameters, it also influences the results of between-group differences in small-world parameters. In addition, at higher network densities, the choice of null network influences the direction of group differences in network measures. Our data suggest that the choice of null network is quite crucial for interpretation of group differences in small-world parameters of structural correlation networks. We argue that none of the available null models is perfect for estimation of small-world parameters for correlation networks and the relative strengths and weaknesses of the selected model should be carefully considered with respect to obtained network measures.
PMCID: PMC3696118  PMID: 23840672
8.  Resolving Structural Variability in Network Models and the Brain 
PLoS Computational Biology  2014;10(3):e1003491.
Large-scale white matter pathways crisscrossing the cortex create a complex pattern of connectivity that underlies human cognitive function. Generative mechanisms for this architecture have been difficult to identify in part because little is known in general about mechanistic drivers of structured networks. Here we contrast network properties derived from diffusion spectrum imaging data of the human brain with 13 synthetic network models chosen to probe the roles of physical network embedding and temporal network growth. We characterize both the empirical and synthetic networks using familiar graph metrics, but presented here in a more complete statistical form, as scatter plots and distributions, to reveal the full range of variability of each measure across scales in the network. We focus specifically on the degree distribution, degree assortativity, hierarchy, topological Rentian scaling, and topological fractal scaling—in addition to several summary statistics, including the mean clustering coefficient, the shortest path-length, and the network diameter. The models are investigated in a progressive, branching sequence, aimed at capturing different elements thought to be important in the brain, and range from simple random and regular networks, to models that incorporate specific growth rules and constraints. We find that synthetic models that constrain the network nodes to be physically embedded in anatomical brain regions tend to produce distributions that are most similar to the corresponding measurements for the brain. We also find that network models hardcoded to display one network property (e.g., assortativity) do not in general simultaneously display a second (e.g., hierarchy). This relative independence of network properties suggests that multiple neurobiological mechanisms might be at play in the development of human brain network architecture. Together, the network models that we develop and employ provide a potentially useful starting point for the statistical inference of brain network structure from neuroimaging data.
Author Summary
White matter tracts crisscrossing the human cortex are linked in a complex pattern that constrains human thought and behavior. Why the human brain displays the complex pattern that it does is a fascinating open question. Progress in uncovering generative mechanisms for this architecture requires greater knowledge about mechanistic drivers of anatomical networks. Here we contrast network properties derived from images of the human brain with 13 synthetic network models investigated in a progressive, branching sequence, chosen to probe the roles of physical embedding and temporal growth. We characterize both the empirical and synthetic networks using network diagnostics presented here in statistical form, as scatter plots and distributions, to reveal the full range of variability of each measure. We find that synthetic models that constrain the network nodes to be physically embedded in anatomical brain regions tend to produce distributions that are most similar to the corresponding measurements for the brain. We also find that network models hardcoded to display one network property do not in general simultaneously display a second, suggesting that multiple neurobiological mechanisms drive human brain network development. The network models that we develop and employ enable statistical inference of brain network structure from neuroimaging data.
PMCID: PMC3967917  PMID: 24675546
9.  Receptor-Defined Subtypes of Breast Cancer in Indigenous Populations in Africa: A Systematic Review and Meta-Analysis 
PLoS Medicine  2014;11(9):e1001720.
In a systematic review and meta-analysis, Isabel dos Santos Silva and colleagues estimate the prevalence of receptor-defined subtypes of breast cancer in North Africa and sub-Saharan Africa.
Please see later in the article for the Editors' Summary
Breast cancer is the most common female cancer in Africa. Receptor-defined subtypes are a major determinant of treatment options and disease outcomes but there is considerable uncertainty regarding the frequency of poor prognosis estrogen receptor (ER) negative subtypes in Africa. We systematically reviewed publications reporting on the frequency of breast cancer receptor-defined subtypes in indigenous populations in Africa.
Methods and Findings
Medline, Embase, and Global Health were searched for studies published between 1st January 1980 and 15th April 2014. Reported proportions of ER positive (ER+), progesterone receptor positive (PR+), and human epidermal growth factor receptor-2 positive (HER2+) disease were extracted and 95% CI calculated. Random effects meta-analyses were used to pool estimates. Fifty-four studies from North Africa (n = 12,284 women with breast cancer) and 26 from sub-Saharan Africa (n = 4,737) were eligible. There was marked between-study heterogeneity in the ER+ estimates in both regions (I2>90%), with the majority reporting proportions between 0.40 and 0.80 in North Africa and between 0.20 and 0.70 in sub-Saharan Africa. Similarly, large between-study heterogeneity was observed for PR+ and HER2+ estimates (I2>80%, in all instances). Meta-regression analyses showed that the proportion of ER+ disease was 10% (4%–17%) lower for studies based on archived tumor blocks rather than prospectively collected specimens, and 9% (2%–17%) lower for those with ≥40% versus those with <40% grade 3 tumors. For prospectively collected samples, the pooled proportions for ER+ and triple negative tumors were 0.59 (0.56–0.62) and 0.21 (0.17–0.25), respectively, regardless of region. Limitations of the study include the lack of standardized procedures across the various studies; the low methodological quality of many studies in terms of the representativeness of their case series and the quality of the procedures for collection, fixation, and receptor testing; and the possibility that women with breast cancer may have contributed to more than one study.
The published data from the more appropriate prospectively measured specimens are consistent with the majority of breast cancers in Africa being ER+. As no single subtype dominates in the continent availability of receptor testing should be a priority, especially for young women with early stage disease where appropriate receptor-specific treatment modalities offer the greatest potential for reducing years of life lost.
Please see later in the article for the Editors' Summary
Editors' Summary
Breast cancer is the commonest female tumor in Africa and death rates from the disease in some African countries are among the highest in the world. Breast cancer begins when cells in the breast acquire genetic changes that allow them to grow uncontrollably and to move around the body. When a breast lump is found (by mammography or manual examination), a few cells are collected from the lump (a biopsy) to look for abnormal cells and to test for the presence of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor-2 (HER2) on the cells. The hormones estrogen and progesterone promote the growth of normal breast cells and of ER+ and PR+ breast cancer cells. HER2 also controls the growth of breast cells. The receptor status of breast cancer is a major determinant of treatment options and prognosis (likely outcome). ER+ tumors, for example, are more receptive to hormonal therapy and have a better prognosis than ER− tumors, whereas HER2+ tumors, which make large amounts of HER2, are more aggressive than HER2− tumors. Breast cancer is treated by surgically removing the lump or the whole breast (mastectomy) if the tumor has already spread, before killing any remaining cancer cells with chemotherapy or radiotherapy. In addition, ER+, PR+, and HER2+ tumors are treated with drugs that block these receptors (including tamoxifen and trastuzumab), thereby slowing breast cancer growth.
Why Was This Study Done?
ER+ tumors predominate in white women but the proportion of ER+ tumors among US-born black women is slightly lower. The frequency of different receptor-defined subtypes of breast cancer in indigenous populations in Africa is currently unclear but policy makers need this information to help them decide whether routine receptor status testing should be introduced across Africa. Because receptor status is a major determination of treatment options and outcomes, it would be more important to introduce receptor testing if all subtypes are present in breast cancers in indigenous African women and if no one subtype dominates than if most breast cancers in these women are ER+. In this systematic review (a study that uses pre-defined criteria to identify all the research on a given topic) and meta-analysis (a statistical approach that combines the results of several studies), the researchers examine the distribution of receptor-defined breast cancer subtypes in indigenous populations in Africa.
What Did the Researchers Do and Find?
The researchers identified 54 relevant studies from North Africa involving 12,284 women with breast cancer (mainly living in Egypt or Tunisia) and 26 studies from sub-Saharan Africa involving 4,737 women with breast cancer (mainly living in Nigeria or South Africa) and used the data from these studies to calculate the proportions of ER+, PR+, and HER2+ tumors (the number of receptor-positive tumors divided by the number of tumors with known receptor status) across Africa. The proportion of ER+ tumors varied markedly between studies, ranging between 0.40 and 0.80 in North Africa and between 0.20 and 0.70 in sub-Saharan Africa. Among prospectively collected samples (samples collected specifically for receptor-status testing; studies that determined the receptor status of breast cancers using stored samples reported a lower proportion of ER+ disease than studies that used prospectively collected samples), the overall pooled proportions of ER+ and triple negative tumors were 0.59 and 0.21, respectively.
What Do These Findings Mean?
Although these findings highlight the scarcity of data on hormone receptor and HER2 status in breast cancers in indigenous African populations, they provide new information about the distribution of breast cancer subtypes in Africa. Specifically, these findings suggest that although slightly more than half of breast cancers in Africa are ER+, no single subtype dominates. They also suggest that the distribution of receptor-defined breast cancer subtypes in Africa is similar to that found in Western populations. The accuracy of these findings is likely to be affected by the low methodological quality of many of the studies and the lack of standardized procedures. Thus, large well-designed studies are still needed to accurately quantify the distribution of various breast cancer subtypes across Africa. In the meantime, the current findings support the introduction of routine receptor testing across Africa, especially for young women with early stage breast cancer in whom the potential to improve survival and reduce the years of life lost by knowing the receptor status of an individual's tumor is greatest.
Additional Information
Please access these websites via the online version of this summary at
This study is further discussed in a PLOS Medicine Perspective by Sulma i Mohammed
The US National Cancer Institute (NCI) provides comprehensive information about cancer (in English and Spanish), including detailed information for patients and professionals about breast cancer including an online booklet for patients
Cancer Research UK, a not-for profit organization, provides information about cancer; its detailed information about breast cancer includes sections on tests for hormone receptors and HER2 and on treatments that target hormone receptors and treatments that target HER2 is a not-for-profit organization that provides up-to-date information about breast cancer (in English and Spanish), including information on hormone receptor status and HER2 status
The UK National Health Service Choices website has information and personal stories about breast cancer; the not-for profit organization Healthtalkonline also provides personal stories about dealing with breast cancer
PMCID: PMC4159229  PMID: 25202974
10.  Altered Cerebral Blood Flow One Month after Systemic Chemotherapy for Breast Cancer: A Prospective Study Using Pulsed Arterial Spin Labeling MRI Perfusion 
PLoS ONE  2014;9(5):e96713.
Cerebral structural and functional alterations have been reported after chemotherapy for non-CNS cancers, yet the causative mechanism behind these changes remains unclear. This study employed a novel, non-invasive, MRI-based neuroimaging measure to provide the first direct longitudinal measurement of resting cerebral perfusion in breast cancer patients, which was tested for association with changes in cognitive function and gray matter density. Perfusion was measured using pulsed arterial spin labeling MRI in women with breast cancer treated with (N = 27) or without (N = 26) chemotherapy and matched healthy controls (N = 26) after surgery before other treatments (baseline), and one month after chemotherapy completion or yoked intervals. Voxel-based analysis was employed to assess perfusion in gray matter; changes were examined in relation to overall neuropsychological test performance and frontal gray matter density changes measured by structural MRI. Baseline perfusion was not significantly different across groups. Unlike control groups, chemotherapy-treated patients demonstrated significantly increased perfusion post-treatment relative to baseline, which was statistically significant relative to controls in the right precentral gyrus. This perfusion increase was negatively correlated with baseline overall neuropsychological performance, but was not associated with frontal gray matter density reduction. However, decreased frontal gray matter density was associated with decreased perfusion in bilateral frontal and parietal lobes in the chemotherapy-treated group. These findings indicate that chemotherapy is associated with alterations in cerebral perfusion which are both related to and independent of gray matter changes. This pattern of results suggests the involvement of multiple mechanisms of chemotherapy-induced cognitive dysfunction. Additionally, lower baseline cognitive function may be a risk factor for treatment-associated perfusion dysregulation. Future research is needed to clarify these mechanisms, identify individual differences in susceptibility to treatment-associated changes, and further examine perfusion change over time in survivors.
PMCID: PMC4016018  PMID: 24816641
11.  Systemic chemotherapy decreases brain glucose metabolism 
Cancer patients may experience neurologic adverse effects, such as alterations in neurocognitive function, as a consequence of chemotherapy. The mechanisms underlying such neurotoxic syndromes remain poorly understood. We here describe the temporal and regional effects of systemically administered platinum-based chemotherapy on glucose metabolism in the brain of cancer patients.
Using sequential FDG-PET/CT imaging prior to and after administration of chemotherapy, we retrospectively characterized the effects of intravenously administered chemotherapy on brain glucose metabolism in a total of 24 brain regions in a homogenous cohort of 10 patients with newly diagnosed non-small-cell lung cancer.
Significant alterations of glucose metabolism were found in response to chemotherapy in all gray matter structures, including cortical structures, deep nuclei, hippocampi, and cerebellum. Metabolic changes were also notable in frontotemporal white matter (WM) network systems, including the corpus callosum, subcortical, and periventricular WM tracts.
Our data demonstrate a decrease in glucose metabolism in both gray and white matter structures associated with chemotherapy. Among the affected regions are those relevant to the maintenance of brain plasticity and global neurologic function. This study potentially offers novel insights into the spatial and temporal effects of systemic chemotherapy on brain metabolism in cancer patients.
PMCID: PMC4241806  PMID: 25493270
12.  Gray matter reduction associated with systemic chemotherapy for breast cancer: a prospective MRI study 
Brain gray matter alterations have been reported in cross-sectional magnetic resonance imaging (MRI) studies of breast cancer patients after cancer treatment. Here we report the first prospective MRI study of women undergoing treatment for breast cancer, with or without chemotherapy, as well as healthy controls. We hypothesized that chemotherapy-associated changes in gray matter density would be detectable 1 month after treatment, with partial recovery 1 year later. Participants included breast cancer patients treated with (CTx+, N = 17) or without (CTx–, N = 12) chemotherapy and matched healthy controls (N = 18). MRI scans were acquired at baseline (after surgery but before radiation, chemotherapy, and/or anti-estrogen treatment), 1 month after completion of chemotherapy (M1), and 1 year later (Y1). Voxel-based morphometry (VBM) was used to evaluate gray matter density differences between groups and over time. There were no between-group gray matter differences at baseline. Group-by-time interactions showed declines from baseline to M1 in both cancer groups relative to controls. Within-group analyses indicated that at M1 relative to baseline the CTx+ group had decreased gray matter density in bilateral frontal, temporal, and cerebellar regions and right thalamus. Recovery was seen at Y1 in some regions, although persistent decreases were also apparent. No significant within-group changes were found in the CTx– or control groups. Findings were not attributable to recency of cancer surgery, disease stage, psychiatric symptoms, psychotropic medication use, or hormonal treatment status. This study is the first to use a prospective, longitudinal approach to document decreased brain gray matter density shortly after breast cancer chemotherapy and its course of recovery over time. These gray matter alterations appear primarily related to the effects of chemotherapy, rather than solely reflecting host factors, the cancer disease process, or effects of other cancer treatments.
PMCID: PMC3661415  PMID: 20690040
Adjuvant chemotherapy; Brain; Breast cancer; Magnetic resonance imaging; Neuroimaging
13.  Elevated prefrontal myo-inositol and choline following breast cancer chemotherapy 
Brain imaging and behavior  2013;7(4):10.1007/s11682-013-9228-1.
Breast cancer survivors are at increased risk for cognitive dysfunction, which reduces quality of life. Neuroimaging studies provide critical insights regarding the mechanisms underlying these cognitive deficits as well as potential biologic targets for interventions. We measured several metabolite concentrations using 1H magnetic resonance spectroscopy as well as cognitive performance in 19 female breast cancer survivors and 17 age-matched female controls. Women with breast cancer were all treated with chemotherapy. Results indicated significantly increased choline (Cho) and myo-inositol (mI) with correspondingly decreased N-acetylaspartate (NAA)/Cho and NAA/mI ratios in the breast cancer group compared to controls. The breast cancer group reported reduced executive function and memory, and subjective memory ability was correlated with mI and Cho levels in both groups. These findings provide preliminary evidence of an altered metabolic profile that increases our understanding of neurobiologic status post-breast cancer and chemotherapy.
PMCID: PMC3731420  PMID: 23536015
MR Spectroscopy; Breast Cancer; Cognition; Prefrontal Cortex; Chemotherapy
14.  Patterns of brain structural connectivity differentiate normal weight from overweight subjects 
NeuroImage : Clinical  2015;7:506-517.
Alterations in the hedonic component of ingestive behaviors have been implicated as a possible risk factor in the pathophysiology of overweight and obese individuals. Neuroimaging evidence from individuals with increasing body mass index suggests structural, functional, and neurochemical alterations in the extended reward network and associated networks.
To apply a multivariate pattern analysis to distinguish normal weight and overweight subjects based on gray and white-matter measurements.
Structural images (N = 120, overweight N = 63) and diffusion tensor images (DTI) (N = 60, overweight N = 30) were obtained from healthy control subjects. For the total sample the mean age for the overweight group (females = 32, males = 31) was 28.77 years (SD = 9.76) and for the normal weight group (females = 32, males = 25) was 27.13 years (SD = 9.62). Regional segmentation and parcellation of the brain images was performed using Freesurfer. Deterministic tractography was performed to measure the normalized fiber density between regions. A multivariate pattern analysis approach was used to examine whether brain measures can distinguish overweight from normal weight individuals.
1. White-matter classification: The classification algorithm, based on 2 signatures with 17 regional connections, achieved 97% accuracy in discriminating overweight individuals from normal weight individuals. For both brain signatures, greater connectivity as indexed by increased fiber density was observed in overweight compared to normal weight between the reward network regions and regions of the executive control, emotional arousal, and somatosensory networks. In contrast, the opposite pattern (decreased fiber density) was found between ventromedial prefrontal cortex and the anterior insula, and between thalamus and executive control network regions. 2. Gray-matter classification: The classification algorithm, based on 2 signatures with 42 morphological features, achieved 69% accuracy in discriminating overweight from normal weight. In both brain signatures regions of the reward, salience, executive control and emotional arousal networks were associated with lower morphological values in overweight individuals compared to normal weight individuals, while the opposite pattern was seen for regions of the somatosensory network.
1. An increased BMI (i.e., overweight subjects) is associated with distinct changes in gray-matter and fiber density of the brain. 2. Classification algorithms based on white-matter connectivity involving regions of the reward and associated networks can identify specific targets for mechanistic studies and future drug development aimed at abnormal ingestive behavior and in overweight/obesity.
•Multivariate analysis can be used to classify overweight from normal weight individuals.•Anatomical connectivity achieved 97% accuracy in the classification algorithm.•Greater connectivity was observed in extended reward and somatosensory regions.•Morphological gray-matter achieved 69% accuracy in the classification algorithm.•Lower morphological values were observed in regions of the extended reward network.
PMCID: PMC4338207  PMID: 25737959
Obesity; Overweight; Morphological gray-matter; Anatomical white-matter connectivity; Reward network; Multivariate analysis; Classification algorithm; HC, healthy control; BMI, body mass index; HAD, hospital anxiety and Depression Scale; TR, repetition time; TE, echo time; FA, flip angle; GLM, general linear model; DWI, diffusion-weighted MRIs; FOV, field of view; GMV, gray matter volume; SA, surface area; CT, cortical thickness; MC, mean curvature; DTI, diffusion tensor imaging; FACT, fiber assignment by continuous tracking; SPSS, statistical package for the social sciences; ANOVA, analysis of variance; FDR, false-discovery rate; sPLS-DA, sparse partial least squares for discrimination Analysis; VIP, variable importance in projection; PPV, positive predictive value; NPV, negative predictive value; VTA, ventral tegmental area; OFG, orbitofrontal gyrus; PPC, posterior parietal cortex; dlPFC, dorsolateral prefrontal cortex; vmPFC, ventromedial prefrontal cortex; aMCC, anterior mid cingulate cortex; sgACC, subgenual anterior cingulate cortex; ACC, anterior cingulate cortex
15.  An Evaluation of the Left-Brain vs. Right-Brain Hypothesis with Resting State Functional Connectivity Magnetic Resonance Imaging 
PLoS ONE  2013;8(8):e71275.
Lateralized brain regions subserve functions such as language and visuospatial processing. It has been conjectured that individuals may be left-brain dominant or right-brain dominant based on personality and cognitive style, but neuroimaging data has not provided clear evidence whether such phenotypic differences in the strength of left-dominant or right-dominant networks exist. We evaluated whether strongly lateralized connections covaried within the same individuals. Data were analyzed from publicly available resting state scans for 1011 individuals between the ages of 7 and 29. For each subject, functional lateralization was measured for each pair of 7266 regions covering the gray matter at 5-mm resolution as a difference in correlation before and after inverting images across the midsagittal plane. The difference in gray matter density between homotopic coordinates was used as a regressor to reduce the effect of structural asymmetries on functional lateralization. Nine left- and 11 right-lateralized hubs were identified as peaks in the degree map from the graph of significantly lateralized connections. The left-lateralized hubs included regions from the default mode network (medial prefrontal cortex, posterior cingulate cortex, and temporoparietal junction) and language regions (e.g., Broca Area and Wernicke Area), whereas the right-lateralized hubs included regions from the attention control network (e.g., lateral intraparietal sulcus, anterior insula, area MT, and frontal eye fields). Left- and right-lateralized hubs formed two separable networks of mutually lateralized regions. Connections involving only left- or only right-lateralized hubs showed positive correlation across subjects, but only for connections sharing a node. Lateralization of brain connections appears to be a local rather than global property of brain networks, and our data are not consistent with a whole-brain phenotype of greater “left-brained” or greater “right-brained” network strength across individuals. Small increases in lateralization with age were seen, but no differences in gender were observed.
PMCID: PMC3743825  PMID: 23967180
16.  Network modeling of the transcriptional effects of copy number aberrations in glioblastoma 
DNA copy number aberrations (CNAs) are a characteristic feature of cancer genomes. In this work, Rebecka Jörnsten, Sven Nelander and colleagues combine network modeling and experimental methods to analyze the systems-level effects of CNAs in glioblastoma.
We introduce a modeling approach termed EPoC (Endogenous Perturbation analysis of Cancer), enabling the construction of global, gene-level models that causally connect gene copy number with expression in glioblastoma.On the basis of the resulting model, we predict genes that are likely to be disease-driving and validate selected predictions experimentally. We also demonstrate that further analysis of the network model by sparse singular value decomposition allows stratification of patients with glioblastoma into short-term and long-term survivors, introducing decomposed network models as a useful principle for biomarker discovery.Finally, in systematic comparisons, we demonstrate that EPoC is computationally efficient and yields more consistent results than mRNA-only methods, standard eQTL methods, and two recent multivariate methods for genotype–mRNA coupling.
Gains and losses of chromosomal material (DNA copy number aberrations; CNAs) are a characteristic feature of cancer genomes. At the level of a single locus, it is well known that increased copy number (gene amplification) typically leads to increased gene expression, whereas decreased copy number (gene deletion) leads to decreased gene expression (Pollack et al, 2002; Lee et al, 2008; Nilsson et al, 2008). However, CNAs also affect the expression of genes located outside the amplified/deleted region itself via indirect mechanisms. To fully understand the action of CNAs, it is therefore necessary to analyze their action in a network context. Toward this goal, improved computational approaches will be important, if not essential.
To determine the global effects on transcription of CNAs in the brain tumor glioblastoma, we develop EPoC (Endogenous Perturbation analysis of Cancer), a computational technique capable of inferring sparse, causal network models by combining genome-wide, paired CNA- and mRNA-level data. EPoC aims to detect disease-driving copy number aberrations and their effect on target mRNA expression, and stratify patients into long-term and short-term survivors. Technically, EPoC relates CNA perturbations to mRNA responses by matrix equations, derived from a steady-state approximation of the transcriptional network. Patient prognostic scores are obtained from singular value decompositions of the network matrix. The models are constructed by solving a large-scale, regularized regression problem.
We apply EPoC to glioblastoma data from The Cancer Genome Atlas (TCGA) consortium (186 patients). The identified CNA-driven network comprises 10 672 genes, and contains a number of copy number-altered genes that control multiple downstream genes. Highly connected hub genes include well-known oncogenes and tumor supressor genes that are frequently deleted or amplified in glioblastoma, including EGFR, PDGFRA, CDKN2A and CDKN2B, confirming a clear association between these aberrations and transcriptional variability of these brain tumors. In addition, we identify a number of hub genes that have previously not been associated with glioblastoma, including interferon alpha 1 (IFNA1), myeloid/lymphoid or mixed-lineage leukemia translocated to 10 (MLLT10, a well-known leukemia gene), glutamate decarboxylase 2 GAD2, a postulated glutamate receptor GPR158 and Necdin (NDN). Furthermore, we demonstrate that the network model contains useful information on downstream target genes (including stem cell regulators), and possible drug targets.
We proceed to explore the validity of a small network region experimentally. Introducing experimental perturbations of NDN and other targets in four glioblastoma cell lines (T98G, U-87MG, U-343MG and U-373MG), we confirm several predicted mechanisms. We also demonstrate that the TCGA glioblastoma patients can be stratified into long-term and short-term survivors, using our proposed prognostic scores derived from a singular vector decomposition of the network model. Finally, we compare EPoC to existing methods for mRNA networks analysis and expression quantitative locus methods, and demonstrate that EPoC produces more consistent models between technically independent glioblastoma data sets, and that the EPoC models exhibit better overlap with known protein–protein interaction networks and pathway maps.
In summary, we conclude that large-scale integrative modeling reveals mechanistically and prognostically informative networks in human glioblastoma. Our approach operates at the gene level and our data support that individual hub genes can be identified in practice. Very large aberrations, however, cannot be fully resolved by the current modeling strategy.
DNA copy number aberrations (CNAs) are a hallmark of cancer genomes. However, little is known about how such changes affect global gene expression. We develop a modeling framework, EPoC (Endogenous Perturbation analysis of Cancer), to (1) detect disease-driving CNAs and their effect on target mRNA expression, and to (2) stratify cancer patients into long- and short-term survivors. Our method constructs causal network models of gene expression by combining genome-wide DNA- and RNA-level data. Prognostic scores are obtained from a singular value decomposition of the networks. By applying EPoC to glioblastoma data from The Cancer Genome Atlas consortium, we demonstrate that the resulting network models contain known disease-relevant hub genes, reveal interesting candidate hubs, and uncover predictors of patient survival. Targeted validations in four glioblastoma cell lines support selected predictions, and implicate the p53-interacting protein Necdin in suppressing glioblastoma cell growth. We conclude that large-scale network modeling of the effects of CNAs on gene expression may provide insights into the biology of human cancer. Free software in MATLAB and R is provided.
PMCID: PMC3101951  PMID: 21525872
cancer biology; cancer genomics; glioblastoma
17.  Neurodevelopmental alterations of large-scale structural networks in children with new-onset epilepsy 
Human brain mapping  2014;35(8):3661-3672.
Recent neuroimaging and behavioral studies have revealed that children with new onset epilepsy already exhibit brain structural abnormalities and cognitive impairment. How the organization of large-scale brain structural networks is altered near the time of seizure onset and whether network changes are related to cognitive performances remain unclear. Recent studies also suggest that regional brain volume covariance reflects synchronized brain developmental changes. Here, we test the hypothesis that epilepsy during early-life is associated with abnormalities in brain network organization and cognition. We used graph theory to study structural brain networks based on regional volume covariance in 39 children with new-onset seizures and 28 healthy controls. Children with new-onset epilepsy showed a suboptimal topological structural organization with enhanced network segregation and reduced global integration compared to controls. At the regional level, structural reorganization was evident with redistributed nodes from the posterior to more anterior head regions. The epileptic brain network was more vulnerable to targeted but not random attacks. Finally, a subgroup of children with epilepsy, namely those with lower IQ and poorer executive function, had a reduced balance between network segregation and integration. Taken together, the findings suggest that the neurodevelopmental impact of new onset childhood epilepsies alters large-scale brain networks, resulting in greater vulnerability to network failure and cognitive impairment.
PMCID: PMC4107168  PMID: 24453089
Epilepsy; structural network; graph theory; neurodevelopment; cognition
18.  Association between Melanocytic Nevi and Risk of Breast Diseases: The French E3N Prospective Cohort 
PLoS Medicine  2014;11(6):e1001660.
Using data from the French E3N prospective cohort, Marina Kvaskoff and colleagues examine the association between number of cutaneous nevi and the risk for breast cancer.
Please see later in the article for the Editors' Summary
While melanocytic nevi have been associated with genetic factors and childhood sun exposure, several observations also suggest a potential hormonal influence on nevi. To test the hypothesis that nevi are associated with breast tumor risk, we explored the relationships between number of nevi and benign and malignant breast disease risk.
Methods and Findings
We prospectively analyzed data from E3N, a cohort of French women aged 40–65 y at inclusion in 1990. Number of nevi was collected at inclusion. Hazard ratios (HRs) for breast cancer and 95% confidence intervals (CIs) were calculated using Cox proportional hazards regression models. Associations of number of nevi with personal history of benign breast disease (BBD) and family history of breast cancer were estimated using logistic regression. Over the period 15 June 1990–15 June 2008, 5,956 incident breast cancer cases (including 5,245 invasive tumors) were ascertained among 89,902 women. In models adjusted for age, education, and known breast cancer risk factors, women with “very many” nevi had a significantly higher breast cancer risk (HR = 1.13, 95% CI = 1.01–1.27 versus “none”; ptrend = 0.04), although significance was lost after adjustment for personal history of BBD or family history of breast cancer. The 10-y absolute risk of invasive breast cancer increased from 3,749 per 100,000 women without nevi to 4,124 (95% CI = 3,674–4,649) per 100,000 women with “very many” nevi. The association was restricted to premenopausal women (HR = 1.40, ptrend = 0.01), even after full adjustment (HR = 1.34, ptrend = 0.03; phomogeneity = 0.04), but did not differ according to breast cancer type or hormone receptor status. In addition, we observed significantly positive dose–response relationships between number of nevi and history of biopsy-confirmed BBD (n = 5,169; ptrend<0.0001) and family history of breast cancer in first-degree relatives (n = 7,472; ptrend = 0.0003). The main limitations of our study include self-report of number of nevi using a qualitative scale, and self-reported history of biopsied BBD.
Our findings suggest associations between number of nevi and the risk of premenopausal breast cancer, BBD, and family history of breast cancer. More research is warranted to elucidate these relationships and to understand their underlying mechanisms.
Please see later in the article for the Editors' Summary
Editors' Summary
In 2012, nearly 1.7 million women worldwide discovered they had breast cancer, and about half a million women died from the disease. Breast cancer begins when cells in the breast acquire genetic changes that allow them to divide uncontrollably and to move around the body (metastasize). Uncontrolled cell division leads to the formation of a lump that can be detected by mammography (a breast X-ray) or by manual breast examination. Breast cancer is treated by surgical removal of the lump, or, if the cancer has started to spread, by removal of the whole breast (mastectomy). Surgery is usually followed by radiotherapy or chemotherapy to kill any remaining cancer cells. Because the female sex hormones estrogen and progesterone stimulate the growth of some tumors, drugs that block hormone receptors are also used to treat receptor-positive breast cancer. Nowadays, the prognosis (outlook) for women with breast cancer is good, and in developed countries, nearly 90% of affected women are still alive five years after diagnosis.
Why Was This Study Done?
Several hormone-related factors affect a woman's chances of developing breast cancer. For example, women who have no children or who have them late in life have a higher breast cancer risk than women who have several children when they are young because pregnancy alters sex hormone levels. Interestingly, the development of moles (nevi)—dark skin blemishes that are risk factors for the development of melanoma, a type of skin cancer—may also be affected by estrogen and progesterone. Thus, the number of nevi might be a marker of blood hormone levels and might predict breast cancer risk. In this prospective cohort study, the researchers test this hypothesis by investigating the association between how many moles a woman has and her breast cancer risk. A prospective cohort study enrolls a group (cohort) of people, determines their baseline characteristics, and follows them over time to see which characteristics are associated with the development of specific diseases.
What Did the Researchers Do and Find?
In 1990, the E3N prospective cohort study enrolled nearly 100,000 French women (mainly school teachers) aged 40–65 years to investigate cancer risk factors. The women completed a baseline questionnaire about their lifestyle and medical history, and regular follow-up questionnaires that asked about cancer occurrence. In the initial questionnaire, the women indicated whether they had no, a few, many, or very many moles. Between 1990 and 2008, nearly 6,000 women in the cohort developed breast cancer. Using statistical methods to calculate hazard ratios (an “HR” compares how often a particular event happens in two groups with different characteristics; an HR greater than one indicates that a specific characteristic is associated with an increased risk of the event), the researchers report that women with “very many” nevi had a significantly higher breast cancer risk (a higher risk that was unlikely to have occurred by chance) than women with no nevi. Specifically, the age-adjusted HR for breast cancer among women with “very many” nevi compared to women with no nevi was 1.17. After adjustment for a personal history of benign (noncancerous) breast disease and a family history of breast cancer (two established risk factors for breast cancer), the association between nevi and breast cancer risk among the whole cohort became nonsignificant. Notably, however, the association among only premenopausal women remained significant after full adjustment (HR = 1.34), which corresponded to an increase in ten-year absolute risk of invasive breast cancer from 2,515 per 100,000 women with no nevi to 3,370 per 100,000 women with “very many” nevi.
What Do These Findings Mean?
These findings suggest that among premenopausal women there is a modest association between nevi number and breast cancer risk. This noncausal relationship may indicate that nevi and breast diseases are affected in similar ways by hormones or share common genetic factors, but the accuracy of these findings may be limited by aspects of the study design. For example, self-report of nevi numbers using a qualitative scale may have introduced some inaccuracies into the estimates of the association between nevi number and breast cancer risk. Most importantly, these findings are insufficient to support the use of nevi counts in breast cancer screening or diagnosis. Rather, together with the findings reported by Zhang et al. in an independent PLOS Medicine Research Article, they suggest that further studies into the biological mechanisms underlying the relationship between nevi and breast cancer and the association itself should be undertaken.
Additional Information
Please access these websites via the online version of this summary at
This study is further discussed in a PLOS Medicine Perspective by Fuhrman and Cardenas
An independent PLOS Medicine Research Article by Zhang et al. also investigates the relationship between nevi number and breast cancer risk
The US National Cancer Institute provides comprehensive information about cancer (in English and Spanish), including detailed information for patients and professionals about breast cancer; it also has a fact sheet on moles
Cancer Research UK, a not-for profit organization, provides information about cancer, including detailed information on breast cancer
The UK National Health Service Choices website has information and personal stories about breast cancer; the not-for profit organization Healthtalkonline also provides personal stories about dealing with breast cancer
More information about the E3N prospective cohort study is available; detailed information is available in French
PMCID: PMC4051602  PMID: 24915306
19.  Frontal gray matter reduction after breast cancer chemotherapy and association with executive symptoms: A replication and extension study 
Brain, behavior, and immunity  2012;30(0):S117-S125.
Cognitive changes related to cancer and its treatment have been intensely studied, and neuroimaging has begun to demonstrate brain correlates. In the first prospective longitudinal neuroimaging study of breast cancer (BC) patients we recently reported decreased gray matter density one month after chemotherapy completion, particularly in frontal regions. These findings helped confirm a neural basis for previously reported cognitive symptoms, which most commonly involve executive and memory processes in which the frontal lobes are a critical component of underlying neural circuitry. Here we present data from an independent, larger, more demographically diverse cohort that is more generalizable to the BC population. BC patients treated with (N = 27) and without (N = 28) chemotherapy and matched healthy controls (N = 24) were scanned at baseline (prior to systemic treatment) and one month following chemotherapy completion (or yoked intervals for non-chemotherapy and control groups) and APOE-genotyped. Voxel-based morphometry (VBM) showed decreased frontal gray matter density after chemotherapy, as observed in the prior cohort, which was accompanied by self-reported difficulties in executive functioning. Gray matter and executive symptom changes were not related to APOE ε4 status, though a somewhat greater percentage of BC patients who received chemotherapy were ε4 allele carriers than patients not treated with chemotherapy or healthy controls. These findings provide confirmatory evidence of frontal morphometric changes that may be a pathophysiological basis for cancer and treatment-related cognitive dysfunction. Further research into individual risk factors for such changes will be critical for development of treatment and prevention strategies.
PMCID: PMC3629547  PMID: 22613170
Adjuvant chemotherapy; APOE genotype; Brain; Breast cancer; BRIEF-A; Executive function; Frontal lobes; Magnetic resonance imaging; Neuroimaging; Voxel-based morphometry
20.  Irritable Bowel Syndrome in female patients is associated with alterations in structural brain networks 
Pain  2013;155(1):137-149.
Alterations in gray matter (GM) density/ volume and cortical thickness (CT) have been demonstrated in small and heterogeneous samples of subjects with different chronic pain syndromes, including irritable bowel syndrome (IBS). Aggregating across 7 structural neuroimaging studies conducted at UCLA between August 2006 and April 2011, we examined group differences in regional GM volume in 201 predominantly premenopausal female subjects (82 IBS, mean age: 32 ± 10 SD, 119 Healthy Controls [HCs], 30± 10 SD). Applying graph theoretical methods and controlling for total brain volume, global and regional properties of large-scale structural brain networks were compared between IBS and HC groups. Relative to HCs, the IBS group had lower volumes in bilateral superior frontal gyrus, bilateral insula, bilateral amygdala, bilateral hippocampus, bilateral middle orbital frontal gyrus, left cingulate, left gyrus rectus, brainstem, and left putamen. Higher volume was found for the left postcentral gyrus. Group differences were no longer significant for most regions when controlling for Early Trauma Inventory global score with the exception of the right amygdala and the left post central gyrus. No group differences were found for measures of global and local network organization. Compared to HCs, the right cingulate gyrus and right thalamus were identified as significantly more critical for information flow. Regions involved in endogenous pain modulation and central sensory amplification were identified as network hubs in IBS. Overall, evidence for central alterations in IBS was found in the form of regional GM volume differences and altered global and regional properties of brain volumetric networks.
PMCID: PMC4100785  PMID: 24076048
chronic pain; irritable bowel syndrome; gray matter volume; brain network analysis; graph theory
21.  A Hybrid CPU-GPU Accelerated Framework for Fast Mapping of High-Resolution Human Brain Connectome 
PLoS ONE  2013;8(5):e62789.
Recently, a combination of non-invasive neuroimaging techniques and graph theoretical approaches has provided a unique opportunity for understanding the patterns of the structural and functional connectivity of the human brain (referred to as the human brain connectome). Currently, there is a very large amount of brain imaging data that have been collected, and there are very high requirements for the computational capabilities that are used in high-resolution connectome research. In this paper, we propose a hybrid CPU-GPU framework to accelerate the computation of the human brain connectome. We applied this framework to a publicly available resting-state functional MRI dataset from 197 participants. For each subject, we first computed Pearson’s Correlation coefficient between any pairs of the time series of gray-matter voxels, and then we constructed unweighted undirected brain networks with 58 k nodes and a sparsity range from 0.02% to 0.17%. Next, graphic properties of the functional brain networks were quantified, analyzed and compared with those of 15 corresponding random networks. With our proposed accelerating framework, the above process for each network cost 80∼150 minutes, depending on the network sparsity. Further analyses revealed that high-resolution functional brain networks have efficient small-world properties, significant modular structure, a power law degree distribution and highly connected nodes in the medial frontal and parietal cortical regions. These results are largely compatible with previous human brain network studies. Taken together, our proposed framework can substantially enhance the applicability and efficacy of high-resolution (voxel-based) brain network analysis, and have the potential to accelerate the mapping of the human brain connectome in normal and disease states.
PMCID: PMC3651094  PMID: 23675425
22.  Associations between age and gray matter volume in anatomical brain networks in middle-aged to older adults 
Aging Cell  2014;13(6):1068-1074.
Aging is associated with cognitive decline, diminished brain function, regional brain atrophy, and disrupted structural and functional brain connectivity. Understanding brain networks in aging is essential, as brain function depends on large-scale distributed networks. Little is known of structural covariance networks to study inter-regional gray matter anatomical associations in aging. Here, we investigate anatomical brain networks based on structural covariance of gray matter volume among 370 middle-aged to older adults of 45–85 years. For each of 370 subjects, we acquired a T1-weighted anatomical MRI scan. After segmentation of structural MRI scans, nine anatomical networks were defined based on structural covariance of gray matter volume among subjects. We analyzed associations between age and gray matter volume in anatomical networks using linear regression analyses. Age was negatively associated with gray matter volume in four anatomical networks (P < 0.001, corrected): a subcortical network, sensorimotor network, posterior cingulate network, and an anterior cingulate network. Age was not significantly associated with gray matter volume in five networks: temporal network, auditory network, and three cerebellar networks. These results were independent of gender and white matter hyperintensities. Gray matter volume decreases with age in networks containing subcortical structures, sensorimotor structures, posterior, and anterior cingulate cortices. Gray matter volume in temporal, auditory, and cerebellar networks remains relatively unaffected with advancing age.
PMCID: PMC4326918  PMID: 25257192
aging; atrophy; brain; gray matter; magnetic resonance imaging; structural covariance networks
23.  Prefrontal Cortex and Executive Function Impairments in Primary Breast Cancer 
Archives of neurology  2011;68(11):1447-1453.
To examine differences in prefrontal-executive function between breast cancer (BC) survivors with and without a history of chemotherapy treatment compared with healthy control women and to determine the associations between prefrontal cortex deficits and behavioral impairments, as well as certain demographic and disease variables.
Observational study.
University-based research facility.
Twenty-five women with BC who had received chemotherapy, 19 women with BC who had not received chemotherapy, and 18 healthy female controls, all matched for age and other demographic variables.
Women with BC demonstrated significantly reduced activation in the left middle dorsolateral prefrontal cortex and premotor cortex compared with healthy controls. The chemotherapy group also demonstrated significantly reduced left caudal lateral prefrontal cortex activation and increased perseverative errors and reduced processing speed compared with the other 2 groups. Reduced left caudal lateral prefrontal cortex activation was significantly correlated with higher disease severity and elevated subjective executive dysfunction in the chemotherapy-treated women. Older age and lower educational level were associated with increased executive function impairment in the chemotherapy group.
These findings provide further evidence of neurological impairment associated with primary BC irrespective of treatment history. The left caudal lateral prefrontal region may be particularly vulnerable to the effects of chemotherapy and/or disease severity and may represent a novel biomarker of subjective executive dysfunction in chemotherapy-treated women. Furthermore, negative effects of chemotherapy on brain function may be exacerbated by such factors as increased age and lower educational level.
PMCID: PMC3239218  PMID: 22084128
24.  Alterations in brain structure and function in breast cancer survivors: effect of post-chemotherapy interval and relation to oxidative DNA damage 
Neuroimaging studies have begun to uncover the neural substrates of cancer and treatment-related cognitive dysfunction, but the time course of these changes in the years following chemotherapy is unclear. This study analyzed multimodality 3T MRI scans to examine the structural and functional effects of chemotherapy and post-chemotherapy interval (PCI) in a cohort of breast cancer survivors (BCS; n=24; PCI mean 6, range 3–10 y) relative to age- and education-matched healthy controls (HC; n=23). Assessments included voxel-based morphometry (VBM) for gray matter density (GMD) and fMRI for activation profile during a 3-back working memory task. The relationships between brain regions associated with PCI and neuropsychological performance, self-reported cognition, and oxidative and direct DNA damage as measured in peripheral lymphocytes were assessed in secondary analyses. PCI was positively associated with GMD and activation on fMRI in the right anterior frontal region (Brodmann Areas 9 and 10) independent of participant age. GMD in this region was also positively correlated with global neuropsychological function. Memory dysfunction, cognitive complaints, and oxidative DNA damage were increased in BCS compared to HC. Imaging results indicated lower fMRI activation in several regions in the BCS group. BCS also had lower GMD than HC in several regions, and in these regions GMD was inversely related to oxidative DNA damage and learning and memory neuropsychological domain scores. This is the first study to show structural and functional effects of PCI and to relate oxidative DNA damage to brain alterations in BCS. The relationship between neuroimaging and cognitive function indicates the potential clinical relevance of these findings. The relationship with oxidative DNA damage provides a mechanistic clue warranting further investigation.
PMCID: PMC3543695  PMID: 23263697
breast cancer; voxel-based morphometry; functional MRI; chemotherapy; DNA damage
25.  Decreased Brain Volume in Adults with Childhood Lead Exposure 
PLoS Medicine  2008;5(5):e112.
Although environmental lead exposure is associated with significant deficits in cognition, executive functions, social behaviors, and motor abilities, the neuroanatomical basis for these impairments remains poorly understood. In this study, we examined the relationship between childhood lead exposure and adult brain volume using magnetic resonance imaging (MRI). We also explored how volume changes correlate with historic neuropsychological assessments.
Methods and Findings
Volumetric analyses of whole brain MRI data revealed significant decreases in brain volume associated with childhood blood lead concentrations. Using conservative, minimum contiguous cluster size and statistical criteria (700 voxels, unadjusted p < 0.001), approximately 1.2% of the total gray matter was significantly and inversely associated with mean childhood blood lead concentration. The most affected regions included frontal gray matter, specifically the anterior cingulate cortex (ACC). Areas of lead-associated gray matter volume loss were much larger and more significant in men than women. We found that fine motor factor scores positively correlated with gray matter volume in the cerebellar hemispheres; adding blood lead concentrations as a variable to the model attenuated this correlation.
Childhood lead exposure is associated with region-specific reductions in adult gray matter volume. Affected regions include the portions of the prefrontal cortex and ACC responsible for executive functions, mood regulation, and decision-making. These neuroanatomical findings were more pronounced for males, suggesting that lead-related atrophic changes have a disparate impact across sexes. This analysis suggests that adverse cognitive and behavioral outcomes may be related to lead's effect on brain development producing persistent alterations in structure. Using a simple model, we found that blood lead concentration mediates brain volume and fine motor function.
Using magnetic resonance imaging to assess brain volumes, Kim Cecil and colleagues find that inner-city children with higher blood lead levels showed regions of decreased gray matter as adults.
Editors' Summary
Lead is a highly toxic metal that is present throughout the environment because of various human activities. In particular, for many years, large amounts of lead were used in paint, in solder for water pipes, in gasoline, and in ceramic glazes. But, as the harmful health effects of lead have become clear, its use in these and other products has been gradually phased out. Breathing air, drinking water, or eating food that contains lead can damage almost every organ in the human body. The organ that is most sensitive to lead exposure is the brain, and children's brains are particularly vulnerable because they are still developing. Children who swallow large amounts of lead can develop widespread brain damage that causes convulsions and sometimes death. Children who are repeatedly exposed to low to moderate amounts of lead (e.g., through accidentally swallowing residues of old lead paint or contaminated soil) can develop learning or behavioral problems.
Why Was This Study Done?
Lead exposure has been linked with various types of brain damage. These include problems with thinking (cognition); difficulties with organizing actions, decisions, and behaviors (executive functions); abnormal social behavior (including aggression); and difficulties in coordinating fine movements, such as picking up small objects (fine motor control). However, we know little about how lead damages the brain in this way and little about which brain regions are affected by exposure to low to moderate levels of lead during childhood. In this study, the researchers wanted to test the possibility that childhood lead exposure might lead to shrinking (“volume loss”) parts of the brain, particularly the parts that are crucial to cognition and behavior. They therefore studied the relationship between childhood lead exposure and adult brain volume. They also explored whether there is a relationship between brain volume and measures of brain functioning, such as fine motor control, memory, and learning assessed during adolescence.
What Did the Researchers Do and Find?
Between 1979 and 1984, the researchers recruited babies born in poor areas of Cincinnati, where there were many old, lead-contaminated houses, into the Cincinnati Lead Study. They measured their blood lead levels regularly from birth until they were 78 months old and calculated each child's average blood lead level over this period. They then used brain scans (known as magnetic resonance imaging, or MRI) to measure the brain volumes of the participants when they were 19–24 years old. The researchers found that exposure to lead as a child was linked with brain volume loss in adulthood, particularly in men. There was a “dose-response” effect—in other words, the greatest brain volume loss was seen in participants with the greatest lead exposure in childhood. The brain volume loss was most noticeable in a part of the brain called the prefrontal cortex—especially a region called the “anterior cingulate cortex.” When they examined the relationship between brain volume and measures of brain functioning, they found a link between brain volume and fine motor control, but not with the other measures.
What Do These Findings Mean?
These findings indicate that childhood lead exposure is associated with brain volume loss in adults, in specific regions of the brain. These brain regions are responsible for executive functions, regulating behavior, and fine motor control. Lead exposure has a larger effect on brain volumes in men than in women, which might help to explain the higher incidence of antisocial behaviors among men than women. Overall, these findings may explain why children and adults who have a history of lead exposure have behavioral and other problems, and support ongoing efforts to reduce childhood lead exposure in the US and other countries.
Additional Information.
Please access these Web sites via the online version of this summary at
A PLoS Medicine Perspective article by David Bellinger further discusses this study and a related paper on child exposure to lead and criminal arrests in adulthood
Toxtown, an interactive site from the US National Library of Medicine, provides information on environmental health concerns including exposure to lead (in English and Spanish)
The US Environmental Protection Agency provides information on lead in paint, dust, and soil and on protecting children from lead poisoning (in English and Spanish)
Medline Plus and the US National Library of Medicine Specialized Information Services provide lists of links to information on lead and human health (in English and Spanish)
The US Centers for Disease Control and Prevention provides information about its Childhood Lead Poisoning Prevention Program
The UK Health Protection Agency also provides information about lead and its health hazards
PMCID: PMC2689675  PMID: 18507499

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