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LINK . SPRINGER . COM {}

  1. Analyzed Page
  2. Matching Content Categories
  3. CMS
  4. Monthly Traffic Estimate
  5. How Does Link.springer.com Make Money
  6. Keywords
  7. Topics
  8. Questions
  9. Schema
  10. External Links
  11. Analytics And Tracking
  12. Libraries
  13. CDN Services

We are analyzing https://link.springer.com/article/10.1007/s10549-011-1702-0.

Title:
Traditional breast cancer risk factors in relation to molecular subtypes of breast cancer | Breast Cancer Research and Treatment
Description:
At least four major categories of invasive breast cancer have been reproducibly identified by gene expression profiling: luminal A, luminal B, HER2-type, and basal-like. These subtypes have been shown to differ in their outcome and response to treatment. Whether this heterogeneity reflects the evolution of these subtypes through distinct etiologic pathways has not been clearly defined. We evaluated the association between traditional breast cancer risk factors and risk of previously defined molecular subtypes of breast cancer in the Nurses’ Health Study. This analysis included 2,022 invasive breast cancer cases for whom we were able to obtain archived breast cancer tissue specimens. Tissue microarrays (TMAs) were constructed, and slides were immunostained for estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), cytokeratin 5/6 (CK5/6), and epidermal growth factor receptor (EGFR). Using immunostain results in combination with histologic grade, cases were grouped into molecularly defined subtypes. We used Cox proportional hazards models to estimate hazard ratios (HRs) and 95% confidence intervals (CIs). We observed differences in the association between risk factors and subtypes of breast cancer. In general, many reproductive factors were most strongly associated with the luminal A subtype, although these differences were not statistically significant. Weight gain since age 18 showed significant differences in its association with molecular subtypes (P-heterogeneity = 0.05) and was most strongly associated with the luminal B subtype (P-trend 0.001). Although there was not significant heterogeneity for lactation across subtypes, an inverse association was strongest for basal-like tumors (HR = 0.6, 95% CI 0.4–0.8; P-heterogeneity = 0.88). These results support the hypothesis that different subtypes of breast cancer have different etiologies and should not be considered as a single group. Identifying risk factors for less common subtypes such as luminal B, HER2-type and basal-like tumors has important implications for prevention of these more aggressive subtypes.
Website Age:
28 years and 1 months (reg. 1997-05-29).

Matching Content Categories {📚}

  • Health & Fitness
  • Education
  • Science

Content Management System {📝}

What CMS is link.springer.com built with?

Custom-built

No common CMS systems were detected on Link.springer.com, and no known web development framework was identified.

Traffic Estimate {📈}

What is the average monthly size of link.springer.com audience?

🌠 Phenomenal Traffic: 5M - 10M visitors per month


Based on our best estimate, this website will receive around 5,000,019 visitors per month in the current month.
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How Does Link.springer.com Make Money? {💸}

We don’t know how the website earns money.

Websites don't always need to be profitable; some serve as platforms for education or personal expression. Websites can serve multiple purposes. And this might be one of them. Link.springer.com might be making money, but it's not detectable how they're doing it.

Keywords {🔍}

cancer, breast, article, google, scholar, pubmed, subtypes, risk, factors, molecular, cas, expression, luminal, health, study, receptor, usa, analysis, research, tamimi, colditz, hankinson, access, natl, res, estrogen, subtype, privacy, cookies, content, data, epidemiology, baer, rosner, basallike, results, clinical, department, publish, search, rulla, hazra, marotti, schnitt, collins, invasive, gene, association, progesterone, human,

Topics {✒️}

month download article/chapter triple-negative breast cancer full article pdf prospective cohort studies van de rijn related subjects breast cancer risk state cancer registries privacy choices/manage cookies invasive breast cancer population-based study gene expression patterns breast cancers breast cancer survivors breast cancer ca089393 perou cm identifying risk factors hormonal risk factors distinct etiologic pathways gene expression profiling tumor molecular subtype human breast carcinoma breast cancer subtypes invasive breast carcinoma tissue microarray technology breast tumor subtypes applying cox regression national cancer institute harvard medical school european economic area estimate hazard ratios check access central laboratory testing composite end points coronary heart disease instant access nurses’ health study washington university school conditions privacy policy progesterone receptor status oxford university press estrogen receptor results poor clinical outcome article log funding/support supported article tamimi accepting optional cookies bimodal frequency distribution breast cancer molecularly defined subtypes

Questions {❓}

  • Brenton JD, Carey LA, Ahmed AA, Caldas C (2005) Molecular classification and molecular forecasting of breast cancer: ready for clinical application?

Schema {🗺️}

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         headline:Traditional breast cancer risk factors in relation to molecular subtypes of breast cancer
         description: At least four major categories of invasive breast cancer have been reproducibly identified by gene expression profiling: luminal A, luminal B, HER2-type, and basal-like. These subtypes have been shown to differ in their outcome and response to treatment. Whether this heterogeneity reflects the evolution of these subtypes through distinct etiologic pathways has not been clearly defined. We evaluated the association between traditional breast cancer risk factors and risk of previously defined molecular subtypes of breast cancer in the Nurses’ Health Study. This analysis included 2,022 invasive breast cancer cases for whom we were able to obtain archived breast cancer tissue specimens. Tissue microarrays (TMAs) were constructed, and slides were immunostained for estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), cytokeratin 5/6 (CK5/6), and epidermal growth factor receptor (EGFR). Using immunostain results in combination with histologic grade, cases were grouped into molecularly defined subtypes. We used Cox proportional hazards models to estimate hazard ratios (HRs) and 95% confidence intervals (CIs). We observed differences in the association between risk factors and subtypes of breast cancer. In general, many reproductive factors were most strongly associated with the luminal A subtype, although these differences were not statistically significant. Weight gain since age 18 showed significant differences in its association with molecular subtypes (P-heterogeneity = 0.05) and was most strongly associated with the luminal B subtype (P-trend 0.001). Although there was not significant heterogeneity for lactation across subtypes, an inverse association was strongest for basal-like tumors (HR = 0.6, 95% CI 0.4–0.8; P-heterogeneity = 0.88). These results support the hypothesis that different subtypes of breast cancer have different etiologies and should not be considered as a single group. Identifying risk factors for less common subtypes such as luminal B, HER2-type and basal-like tumors has important implications for prevention of these more aggressive subtypes.
         datePublished:2011-08-10T00:00:00Z
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      headline:Traditional breast cancer risk factors in relation to molecular subtypes of breast cancer
      description: At least four major categories of invasive breast cancer have been reproducibly identified by gene expression profiling: luminal A, luminal B, HER2-type, and basal-like. These subtypes have been shown to differ in their outcome and response to treatment. Whether this heterogeneity reflects the evolution of these subtypes through distinct etiologic pathways has not been clearly defined. We evaluated the association between traditional breast cancer risk factors and risk of previously defined molecular subtypes of breast cancer in the Nurses’ Health Study. This analysis included 2,022 invasive breast cancer cases for whom we were able to obtain archived breast cancer tissue specimens. Tissue microarrays (TMAs) were constructed, and slides were immunostained for estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), cytokeratin 5/6 (CK5/6), and epidermal growth factor receptor (EGFR). Using immunostain results in combination with histologic grade, cases were grouped into molecularly defined subtypes. We used Cox proportional hazards models to estimate hazard ratios (HRs) and 95% confidence intervals (CIs). We observed differences in the association between risk factors and subtypes of breast cancer. In general, many reproductive factors were most strongly associated with the luminal A subtype, although these differences were not statistically significant. Weight gain since age 18 showed significant differences in its association with molecular subtypes (P-heterogeneity = 0.05) and was most strongly associated with the luminal B subtype (P-trend 0.001). Although there was not significant heterogeneity for lactation across subtypes, an inverse association was strongest for basal-like tumors (HR = 0.6, 95% CI 0.4–0.8; P-heterogeneity = 0.88). These results support the hypothesis that different subtypes of breast cancer have different etiologies and should not be considered as a single group. Identifying risk factors for less common subtypes such as luminal B, HER2-type and basal-like tumors has important implications for prevention of these more aggressive subtypes.
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         Risk factors
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            name:Beth Israel Deaconess Medical Center and Harvard Medical School
            address:
               name:Department of Pathology, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, USA
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External Links {🔗}(110)

Analytics and Tracking {📊}

  • Google Tag Manager

Libraries {📚}

  • Clipboard.js
  • Prism.js

CDN Services {📦}

  • Crossref

4.39s.