Pub Date : 2010-10-01DOI: 10.1158/1055-9965.DISP-10-A55
J. Kaur, R. Vierkant, S. Myers
Introduction: Breast cancer is a major cause of cancer mortality in American Indian and Alaska Native (AIAN) women. However regional differences are striking with lowers rates in Arizona and highest in Alaska with almost a three-fold difference in incidence and mortality between the two states. These differences may be due in part to varying levels of biologic tumor aggressiveness. To evaluate this, we compared a panel of biomarkers on consecutively diagnosed AIAN breast cancer cases from AZ (N=53) and AK (N=42). Methods: Retrospective analysis of tissue blocks measured expression levels for the following panel of biomarkers: ER and PR (ordinally coded as positive vs. negative); her2, BCL-2, and EGFR (coded 0.1.2. and 3+) and P53, MIB-1 and cyclin D (continuous percent of cells stained). Distributions of biomarker values were compared across state of residence using t-tests for continuous and ordinally scaled markers and chi-square tests of significance for binary markers. Age adjusted analyses were also carried out using linear and logistic regression models as appropriate to account for possible differences in age at diagnosis across states. Chart reviews recorded demographics and treatment characteristics. Results: The following demographics were observed with 95 cases of AIAN women with breast cancer analyzed. Average age at diagnosis was similar in the two states (mean, 58.4 for AZ vs. 56.1 for AK, t-test p value=0.45). 74% presented with a palpable mass. 32% had lumpectomy and axillary node dissection. 28% were premenopausal. 8% had a first-degree relative with breast cancer. 46% received adjuvant chemotherapy. 54% received adjuvant hormonal therapy. Cases from AK had higher levels of p53 staining (40.3 vs. 18.5, p=0.004) and lower levels of both EGFR (mean ordinal scaling 0.15 vs. 0.53, p=0.02) and Her2 (mean ordinal scaling 0.81 vs. 1.32, p=0.02) tan those from AZ. No differences in distribution were observed for MIB-1, Cyclin D, BCL-2, ER or PR. When examined together, the triple negative combination of ER/PR/Her2 also did not differ across states (12% for AK vs.13%forAZ, p=0.85). Conclusions: Our findings indicate that regional differences in biomarker expression levels of P53, EGFR and Her2 may exist in AIAN women. Further research is needed to confirm our results and determine to what extent these differences may explain the observed differences in mortality. Genetic testing for BRCA1,2 or other genetic associations with breast cancer have not been done in these populations and may also be useful to examine the reasons for differences in incidence and mortality. In addition, AIAN women are more likely to present with palpable masses representing higher risk stages of breast cancer. Outreach activities in this population continue to be highly important to change mortality. Supported in part by NCI U01 114609 Spirit of Eagles Community Network Program Citation Information: Cancer Epidemiol Biomarkers Prev 2010;19(10 Suppl):A55.
{"title":"Abstract A55: Regional differences in breast cancer biomarkers in American Indian and Alaska Native women","authors":"J. Kaur, R. Vierkant, S. Myers","doi":"10.1158/1055-9965.DISP-10-A55","DOIUrl":"https://doi.org/10.1158/1055-9965.DISP-10-A55","url":null,"abstract":"Introduction: Breast cancer is a major cause of cancer mortality in American Indian and Alaska Native (AIAN) women. However regional differences are striking with lowers rates in Arizona and highest in Alaska with almost a three-fold difference in incidence and mortality between the two states. These differences may be due in part to varying levels of biologic tumor aggressiveness. To evaluate this, we compared a panel of biomarkers on consecutively diagnosed AIAN breast cancer cases from AZ (N=53) and AK (N=42). Methods: Retrospective analysis of tissue blocks measured expression levels for the following panel of biomarkers: ER and PR (ordinally coded as positive vs. negative); her2, BCL-2, and EGFR (coded 0.1.2. and 3+) and P53, MIB-1 and cyclin D (continuous percent of cells stained). Distributions of biomarker values were compared across state of residence using t-tests for continuous and ordinally scaled markers and chi-square tests of significance for binary markers. Age adjusted analyses were also carried out using linear and logistic regression models as appropriate to account for possible differences in age at diagnosis across states. Chart reviews recorded demographics and treatment characteristics. Results: The following demographics were observed with 95 cases of AIAN women with breast cancer analyzed. Average age at diagnosis was similar in the two states (mean, 58.4 for AZ vs. 56.1 for AK, t-test p value=0.45). 74% presented with a palpable mass. 32% had lumpectomy and axillary node dissection. 28% were premenopausal. 8% had a first-degree relative with breast cancer. 46% received adjuvant chemotherapy. 54% received adjuvant hormonal therapy. Cases from AK had higher levels of p53 staining (40.3 vs. 18.5, p=0.004) and lower levels of both EGFR (mean ordinal scaling 0.15 vs. 0.53, p=0.02) and Her2 (mean ordinal scaling 0.81 vs. 1.32, p=0.02) tan those from AZ. No differences in distribution were observed for MIB-1, Cyclin D, BCL-2, ER or PR. When examined together, the triple negative combination of ER/PR/Her2 also did not differ across states (12% for AK vs.13%forAZ, p=0.85). Conclusions: Our findings indicate that regional differences in biomarker expression levels of P53, EGFR and Her2 may exist in AIAN women. Further research is needed to confirm our results and determine to what extent these differences may explain the observed differences in mortality. Genetic testing for BRCA1,2 or other genetic associations with breast cancer have not been done in these populations and may also be useful to examine the reasons for differences in incidence and mortality. In addition, AIAN women are more likely to present with palpable masses representing higher risk stages of breast cancer. Outreach activities in this population continue to be highly important to change mortality. Supported in part by NCI U01 114609 Spirit of Eagles Community Network Program Citation Information: Cancer Epidemiol Biomarkers Prev 2010;19(10 Suppl):A55.","PeriodicalId":9487,"journal":{"name":"Cancer Epidemiology and Prevention Biomarkers","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2010-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80718643","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Donna Kenerson, M. Hargreaves, Kushal A. Patel, C. Larson, J. Drake, Venita Bush
B41 Purpose: The aim of this research was to explore whether key dimensions of social capital might inform the community based participatory research (CBPR) approach to cancer prevention and control interventions that target low-income African American communities. Methodology: Focus groups were conducted at community health centers located in Nashville, Chattanooga, and Memphis. African American men and women, representing low-income urban communities, participated in 54 focus groups. The focus groups used semi-structured questions integrating multiple health-related constructs of social capital with community perceptions centering on the facilitators and barriers to cancer control and prevention. These constructs included community groups, neighborhoods, networks, collective action, communication, and leadership. Results: Participants characterized community as going beyond physical boundaries to interactions among neighbors. Varied beliefs were expressed about the decline in neighborhood cohesion that included changes in racial or ethnic composition and gentrification. Church pastors and politicians were considered leaders of the community; however, church leaders were perceived as more active in the health-related needs of their communities. The poor health of communities was attributed to a lack of motivation by community members to change lifestyles and behaviors. Perceptions surrounding cancer included the belief that cancer was a death sentence and people generally did not want to know if they had cancer. On the other hand, some believed that an early diagnosis of cancer might improve one’s survival, and that there needed to be greater communication of this fact. Major barriers to cancer-related screening included the fear of cancer screening outcomes and the general lack of knowledge related to health screening guidelines. Conclusion: Findings suggest the need for greater awareness of the social processes affecting the health of communities. This heightened awareness can improve the understanding of health and health inequalities affected by social capital. The assessment of social capital, as a health-related construct, may enhance community based participatory research focusing on cancer disparities by creating opportunities for individual and group interactions that facilitate effective and sustainable community health action. Funded by NIH 5P20 MD 000516 (EXPORT), NCI U01 CA114641 (Community Networks Program)
{"title":"Social capital as a framework for community based participatory research in cancer prevention and control","authors":"Donna Kenerson, M. Hargreaves, Kushal A. Patel, C. Larson, J. Drake, Venita Bush","doi":"10.1037/e520982012-008","DOIUrl":"https://doi.org/10.1037/e520982012-008","url":null,"abstract":"B41 Purpose: The aim of this research was to explore whether key dimensions of social capital might inform the community based participatory research (CBPR) approach to cancer prevention and control interventions that target low-income African American communities.\u2028 Methodology: Focus groups were conducted at community health centers located in Nashville, Chattanooga, and Memphis. African American men and women, representing low-income urban communities, participated in 54 focus groups. The focus groups used semi-structured questions integrating multiple health-related constructs of social capital with community perceptions centering on the facilitators and barriers to cancer control and prevention. These constructs included community groups, neighborhoods, networks, collective action, communication, and leadership.\u2028 Results:\u2028 Participants characterized community as going beyond physical boundaries to interactions among neighbors. Varied beliefs were expressed about the decline in neighborhood cohesion that included changes in racial or ethnic composition and gentrification. Church pastors and politicians were considered leaders of the community; however, church leaders were perceived as more active in the health-related needs of their communities. The poor health of communities was attributed to a lack of motivation by community members to change lifestyles and behaviors. Perceptions surrounding cancer included the belief that cancer was a death sentence and people generally did not want to know if they had cancer. On the other hand, some believed that an early diagnosis of cancer might improve one’s survival, and that there needed to be greater communication of this fact. Major barriers to cancer-related screening included the fear of cancer screening outcomes and the general lack of knowledge related to health screening guidelines.\u2028 Conclusion: Findings suggest the need for greater awareness of the social processes affecting the health of communities. This heightened awareness can improve the understanding of health and health inequalities affected by social capital. The assessment of social capital, as a health-related construct, may enhance community based participatory research focusing on cancer disparities by creating opportunities for individual and group interactions that facilitate effective and sustainable community health action.\u2028 Funded by NIH 5P20 MD 000516 (EXPORT), NCI U01 CA114641 (Community Networks Program)","PeriodicalId":9487,"journal":{"name":"Cancer Epidemiology and Prevention Biomarkers","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2007-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74929204","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2007-04-01DOI: 10.1016/S0022-5347(18)31948-7
F. Bianco, B. McHone, K. Wagner, J. Burgess, T. Jarrett, S. Patierno
{"title":"Sexual health inventory in men screened for prostate cancer","authors":"F. Bianco, B. McHone, K. Wagner, J. Burgess, T. Jarrett, S. Patierno","doi":"10.1016/S0022-5347(18)31948-7","DOIUrl":"https://doi.org/10.1016/S0022-5347(18)31948-7","url":null,"abstract":"","PeriodicalId":9487,"journal":{"name":"Cancer Epidemiology and Prevention Biomarkers","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2007-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74661100","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2006-12-01DOI: 10.1200/jco.2007.25.18_suppl.10590
A. Barrier, P. Böelle, D. Brault, A. Flahault, S. Dudoit, A. Lemoine
B20 Purpose. This study aimed to assess the possibility to build a microarray-based prognosis predictor (PP) for stage III colon cancer that could be used to guide postoperative chemotherapy. Material and methods. Thirty-six patients, operated on for a stage III colon cancer, were included in this study. Eighteen patients have subsequently developed a liver metastasis, while the other 18 have remained disease-free for at least 5 years. Tumor mRNA samples were profiled using the Affymetrix HGU133A GeneChip. Patients were repeatedly and randomly divided into 10,000 training (TS) and validation sets (VS) of 10 different sizes. For each TS/VS split, a 30-gene prognosis predictor (PP), identified on the TS by selecting the 30 most differentially expressed genes and applying diagonal linear discriminant analysis, was used to predict the prognoses of VS patients. Results. The 10,000 30-gene PP yielded the following average prognosis prediction performance measures: 72.9% accuracy, 72.2% sensitivity, 73.6% specificity. Improvements in prognosis prediction were observed with increasing TS size (76.1% accuracy, 75.2% sensitivity, and 77.1% specificity for TS of size 32). The 30-gene PP were found to be highly-variable in composition across TS/VS splits. A total of 7,096 genes were included in the 10,000 PP; the higher number of selections for a gene was 5,896. Conclusions. Microarray gene expression profiling is able to predict the prognosis of stage III colon cancer patients and, thus, might be used to guide adjuvant chemotherapy.
{"title":"Stage III colon cancer prognosis prediction by gene expression profiling.","authors":"A. Barrier, P. Böelle, D. Brault, A. Flahault, S. Dudoit, A. Lemoine","doi":"10.1200/jco.2007.25.18_suppl.10590","DOIUrl":"https://doi.org/10.1200/jco.2007.25.18_suppl.10590","url":null,"abstract":"B20 Purpose. This study aimed to assess the possibility to build a microarray-based prognosis predictor (PP) for stage III colon cancer that could be used to guide postoperative chemotherapy. Material and methods. Thirty-six patients, operated on for a stage III colon cancer, were included in this study. Eighteen patients have subsequently developed a liver metastasis, while the other 18 have remained disease-free for at least 5 years. Tumor mRNA samples were profiled using the Affymetrix HGU133A GeneChip. Patients were repeatedly and randomly divided into 10,000 training (TS) and validation sets (VS) of 10 different sizes. For each TS/VS split, a 30-gene prognosis predictor (PP), identified on the TS by selecting the 30 most differentially expressed genes and applying diagonal linear discriminant analysis, was used to predict the prognoses of VS patients. Results. The 10,000 30-gene PP yielded the following average prognosis prediction performance measures: 72.9% accuracy, 72.2% sensitivity, 73.6% specificity. Improvements in prognosis prediction were observed with increasing TS size (76.1% accuracy, 75.2% sensitivity, and 77.1% specificity for TS of size 32). The 30-gene PP were found to be highly-variable in composition across TS/VS splits. A total of 7,096 genes were included in the 10,000 PP; the higher number of selections for a gene was 5,896. Conclusions. Microarray gene expression profiling is able to predict the prognosis of stage III colon cancer patients and, thus, might be used to guide adjuvant chemotherapy.","PeriodicalId":9487,"journal":{"name":"Cancer Epidemiology and Prevention Biomarkers","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2006-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81965830","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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