Genotoxic and Cytotoxic Damage by Cyclophosphamide and Adriamycin as a Response to Treatment in Breast Cancer Patients: Pilot Study

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The aim of this study was to evaluate the genotoxicity induced by cyclophosphamide-adriamycin treatment in breast cancer patients through the frequencies of Sister Chromatid Exchange (SCE), Replication Index (RI), Mitotic Index (MI) and Cell Proliferation Index (CPI) and to study the possible association between biomarkers of genotoxicity and the early response to treatment. The frequencies were obtained before and immediately after therapy from 17 patients with breast cancer (p < 0.001). Response to treatment was assessed after two years resulting in 12 patients in a state of remission. MI and CPI had high values after treatment in women with active cancers compared to those in a state of remission, however there were not significant differences. Conclusions: It is possible that MI y CPI biomarkers can serve as indicators for early assessment of treatment with cyclophosphamide-adriamycin. It should be noted that these are preliminary results and further study is necessary.

Cite this paper

Garibay-Garcia, J. , Mejia-Sanchez, F. , Ramírez-San-Juan, E. , Flores-Merino, M. and Castillo-Cadena, J. (2015) Genotoxic and Cytotoxic Damage by Cyclophosphamide and Adriamycin as a Response to Treatment in Breast Cancer Patients: Pilot Study. Journal of Cancer Therapy, 6, 163-168. doi: 10.4236/jct.2015.62018.

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Risk Factors for Breast Cancer of Different Age Groups: Moroccan Data?

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ABSTRACT

Background: Several epidemiological studies have been carried to identify the risk factors for developing breast cancer. The aim of this study was to evaluate the risk factors associated with breast cancer in different age groups in the Moroccan women and to determine susceptible and protective factors. Methods: This was a case control study including 400 women with breast cancer and 400 controls of the National Institute of Oncology Sidi Mohamed Ben Abdallah, Rabat, Morocco from 2008 to 2010. These women were interviewed for epidemiological information and risk factor for breast cancer. Results: Early age at menarche, late menopause, oral contraceptive and family history with first degree were positively associated with breast cancer in all the series and in the age group between 22 and 34 years. In the age group between 44 and 34 years, a risk of breast cancer was associated only with a family history of breast cancer. In the age group of 45 years or older, the factors which seem to influence a breast cancer are: late menopause, oral contraceptive and family history in the first degree. In contrast, early childbearing, multiparity and lactation were negatively associated with breast cancer. Conclusion: This study is rather in favour of positive association between hormonal factors and breast cancer, and confirms the protective role of multiparity and lactation.

Cite this paper

Laamiri, F. , Bouayad, A. , Hasswane, N. , Ahid, S. , Mrabet, M. and Amina, B. (2015) Risk Factors for Breast Cancer of Different Age Groups: Moroccan Data?. Open Journal of Obstetrics and Gynecology, 5, 79-87. doi: 10.4236/ojog.2015.52011.

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http://dx.doi.org/10.1016/S0140-6736(01)06524-2                                              eww150202lx

An Autoantibody Based Protein Microarray Blood Test to Enhance the Specificity of a Negative Screening Mammogram

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http://www.scirp.org/journal/PaperInformation.aspx?PaperID=53458#.VMW12yzQrzE

ABSTRACT

Background: Current screening mammography for breast cancer is associated with misdiagnosis in as many as 30% of cases. Objectives: To develop and clinically evaluate a unique autoantibody based protein microarray blood test to improve the accuracy of breast cancer screening. Materials and Methods: A microarray was constructed from commercial antigens and antigens selected from screened cDNA libraries of breast cancer tissue samples. A training set containing 439 healthy controls and 276 biopsy proven breast cancer cases was used to establish a set of separating models between the two groups. These models were used to assign a diagnosis to 285 blind samples from 120 breast cancer patients and 165 healthy controls. Results: The test identified 82 of the 120 breast cancer patients and 160 of the 165 healthy controls. These results can be translated into a sensitivity of 68.3% [CI: 59% -77%] and a specificity of 97% [CI: 93% -99%], with a PPV for this validation set of 94.3% (CI: 87.10% -98.11%), NPV of 80.81% [CI: 74.62% -86.05%] and an AUC of 89.2% [CI: 78% -87%]. Conclusions: The protein microarray can be utilized to reduce the false negative rate of routine screening mammography. Women with a negative mammography and a negative blood test can be reassured and encouraged to continue routine breast cancer screening. A positive test should alert the physician about the possible presence of a breast cancer not detected by routine screening mammography and drive to perform additional investigation, such as breast ultrasound and MRI.

Cite this paper

Allweis, T. , Strauss, L. , Malyutin, Z. , Kapov-Kagan, A. , Novikov, I. , Bevers, T. , Iacobelli, S. , Sandri, M. , Bitterman, A. , Engelman, P. , Piura, B. , Rosenberg, M. and Yahalom, G. (2015) An Autoantibody Based Protein Microarray Blood Test to Enhance the Specificity of a Negative Screening Mammogram. Advances in Breast Cancer Research, 4, 22-38. doi: 10.4236/abcr.2015.41003.

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The Association of Night Shift Work with the Development of Breast Cancer in Women

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http://www.scirp.org/journal/PaperInformation.aspx?PaperID=53385#.VMBtkyzQrzE

ABSTRACT

Breast cancer is a serious public health concern in South Africa and globally. It is estimated that one in seven South Africans will develop cancer in their lifetime. According to a case-controlled study, 80% of cancer cases are thought to be due to external, non-inherited factors, which could potentially have been prevented. The objectives of the current case-control study were: 1) to determine the relationship between night shift work and the development of breast cancer; 2) to explore the relationship between night shift work and other types of cancer; 3) to explore any difference between night shift work and breast cancer, and night shift work and other types of cancer. A total of 106 research participants were selected using non-probability, convenience sampling methods and interviewed using a structured questionnaire. Seventy-two (68%) of the women who were interviewed were black, while 32% (n = 35) were white. Of the 106 research participants, 82% (n = 87) had a history of being employed, while 18% (n = 19) had never been employed. Analysed data showed that 29% (n = 31) of the women had a history of working night shift. Of the 31 research participants who reported having worked night shift, 90% (n = 28) had actually done rotating shift work, rather than regular night shift work. The odds ratio of working night shift was found to be 1.24 (OR = 1.24, p = 0.615) higher in breast cancer research participants compared to research participants diagnosed with other types of cancer—odds ratio of 0.80 (p = 0.610). For rotational work, the OR was 1.445, indicating a higher risk than for shift work. It is recommended that the relationship between working night shift and breast cancer risk be explored further through cross-sectional and cohort studies.

Cite this paper

Moukangoe, P. and Jansen van Rensburg, M. (2015) The Association of Night Shift Work with the Development of Breast Cancer in Women. Open Journal of Epidemiology, 5, 14-21. doi: 10.4236/ojepi.2015.51003.

References

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[4] Urban, M., Banks, E., Egger, S., Canfell, K., O’Connell, D., Beral, V. and Sitas, F. (2012) Injectable and Oral Contraceptive Use and Cancers of the Breast, Cervix, Ovary, and Endometrium in Black South African Women: Case-Control Study. PLOS Medicine, 9, 1-11.
[5] Cancer Association of South Africa (2010) Overview of the Latest National Cancer Registry Statistics. Leading Five Female Cancers in South Africa.
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[6] Straif, K., Baan, R., Grosse, Y., Secretan, B., Ghissassi, F., Bouvard, V., Altieri, A., Benbrahim-Tallal, L. and Cogliano, V. (2007) Carcinogenicity of Shift-Work, Painting, and Fire-Fighting. Lancet Oncology, 8, 1065-1066.
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[7] Stevens, R.G. (2009) Light-at-Night, Circadian Disruption and Breast Cancer: Assessment of Existing Evidence. International Journal of Epidemiology, 38, 963-970.
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[13] Grundy, A., Richardson, H., Burstyn, I., Lohrisch, C., SenGupta, S.K., Lai, A.S., Lee, D., Spinelli, J.J. and Aroson, K.J. (2013) Increased Risk of Breast Cancer Associated with Long-Term Shift Work in CANADA. Occupational and Environmental Medicine, 70, 831-838.
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http://dx.doi.org/10.1093/aje/kwq029
[19] Groot, M.T., Baltussen, R., Uyl-de Groot, C.A., Anderson, B.O. and Hortobagyi, G.N. (2005) Costs and Health Effects of Breast Cancer Interventions in Epidemiologically Different Regions of Africa, North America, and Asia. 2nd Biennial Global Summit Consensus Conference on International Breast Health Care, Bethesda, 12-15 January 2005, 581-590.                                                                                         eww150122lx

Observer Variability in BI-RADS Ultrasound Features and Its Influence on Computer-Aided Diagnosis of Breast Masses

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http://www.scirp.org/journal/PaperInformation.aspx?PaperID=53047#.VLSJdsnQrzE

ABSTRACT

Objective: Computer classification of sonographic BI-RADS features can aid differentiation of the malignant and benign masses. However, the variability in the diagnosis due to the differences in the observed features between the observations is not known. The goal of this study is to measure the variation in sonographic features between multiple observations and determine the effect of features variation on computer-aided diagnosis of the breast masses. Materials and Methods: Ultrasound images of biopsy proven solid breast masses were analyzed in three independent observations for BI-RADS sonographic features. The BI-RADS features from each observation were used with Bayes classifier to determine probability of malignancy. The observer agreement in the sonographic features was measured by kappa coefficient and the difference in the diagnostic performances between observations was determined by the area under the ROC curve, Az, and interclass correlation coefficient. Results: While some features were repeatedly observed, κ = 0.95, other showed a significant variation, κ = 0.16. For all features, combined intra-observer agreement was substantial, κ = 0.77. The agreement, however, decreased steadily to 0.66 and 0.56 as time between the observations increased from 1 to 2 and 3 months, respectively. Despite the variation in features between observations the probabilities of malignancy estimates from Bayes classifier were robust and consistently yielded same level of diagnostic performance, Az was 0.772-0.817 for sonographic features alone and 0.828-0.849 for sonographic features and age combined. The difference in the performance, ΔAz, between the observations for the two groups was small (0.003-0.044) and was not statistically significant (p < 0.05). Interclass correlation coefficient for the observations was 0.822 (CI: 0.787-0.853) for BI-RADS sonographic features alone and for those combined with age was 0.833 (CI: 0.800-0.862). Conclusion: Despite the differences in the BI-RADS sonographic features between different observations, the diagnostic performance of computer-aided analysis for differentiating breast masses did not change. Through continual retraining, the computer-aided analysis provides consistent diagnostic performance independent of the variations in the observed sonographic features.

Cite this paper

Sultan, L. , Bouzghar, G. , Levenback, B. , Faizi, N. , Venkatesh, S. , Conant, E. and Sehgal, C. (2015) Observer Variability in BI-RADS Ultrasound Features and Its Influence on Computer-Aided Diagnosis of Breast Masses. Advances in Breast Cancer Research, 4, 1-8. doi: 10.4236/abcr.2015.41001.

References

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[4] Shen, W.C., Chang, R.F. and Moon, W.K. (2007) Computer Aided Classification System for Breast Ultrasound Based on Breast Imaging Reporting and Data System (BI-RADS). Ultrasound in Medicine & Biology, 33, 1688-1698. http://dx.doi.org/10.1016/j.ultrasmedbio.2007.05.016
[5] Moon, W.K., Lo, C.M., Chang, J.M., Huang, C.S., Chen, J.H. and Chang, R.F. (2012) Computer-Aided Classification of Breast Masses Using Speckle Features of Automated Breast Ultrasound Images. Medical Physics, 39, 6465-6473. http://dx.doi.org/10.1118/1.4754801
[6] Moon, W.K., Lo, C.-M., Chang, J.M., Huang, C.-S., Chen, J.-H. and Chang, R.-F. (2013) Quantitative Ultrasound Analysis for Classification of BI-RADS Category 3 Breast Masses. Journal of Digital Imaging, 26, 1091-1098. http://dx.doi.org/10.1007/s10278-013-9593-8
[7] Bouzghar, G., Levenback, B.J., Sultan, L.R., Venkatesh, S.S., Cwanger, A., Conant, E.F. and Sehgal, C.M. (2014) Bayesian Probability of Malignancy with Breast Ultrasound BI-RADS Features. Journal of Ultrasound in Medicine, 33, 641-648. http://dx.doi.org/10.7863/ultra.33.4.641
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[12] Cary, T.W., Cwanger, A., Venkatesh, S.S., Conant, E.F. and Sehgal, C.M. (2012) Comparison of Naive Bayes and Logistic Regression for Computer-Aided Diagnosis of Breast Masses Using Ultrasound Imaging. In: Bosch, J.G. and Doyley, M.M., Eds., Medical Imaging: Ultrasonic Imaging, Tomography, and Therapy, SPIE, Bellingham.
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[14] Abdullah, N., Mesurolle, B., El-Khoury, M. and Kao, E. (2009) Breast Imaging Reporting and Data System Lexicon for US: Interobserver Agreement for Assessment of Breast Masses. Radiology, 252, 665-672.
[15] Calas, M.J., Almeida, R.M., Gutfilen, B. and Pereira, W.C. (2009) Intra-Observer Interpretation of Breast Ultrasonography Following the BI-RADS Classification. European Journal of Radiology, 74, 525-528. http://dx.doi.org/10.1016/j.ejrad.2009.04.015
[16] Park, C.S., Lee, J.H., Yim, H.W., Kang, B.J., Kim, H.S., Jung, J.I., Jung, N.Y. and Kim, S.H. (2007) Observer Agreement Using the ACR Breast Imaging Reporting and Data System (BI-RADS)-Ultrasound. Korean Journal of Radiology, 8, 397-402.
[17] Lee, H.J., Kim, E.K., Kim, M.J., Youk, J.H., Lee, J.Y., Kang, D.R. and Oh, K.K. (2008) Observer Variability of Breast Imaging Reporting and Data System (BI-RADS) for Breast Ultrasound. European Journal of Radiology, 65, 293-298. http://dx.doi.org/10.1016/j.ejrad.2007.04.008
[18] Ryan, J.T., Haygood, T.M., Yamal, J.M., Evanoff, M., O’Sullivan, P., McEntee, M. and Brennan, P.C. (2011) The “Memory Effect” for Repeated Radiologic Observations. American Journal of Roent- genology, 197, W985-W991. http://dx.doi.org/10.2214/AJR.10.5859                                                                             eww150113lx

Inhibition of Proliferation of Breast Cancer Cells MCF7 and MDA-MB-231 by Lipophilic Extracts of Papaya (Carica papaya L. var. Maradol) Fruit

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http://www.scirp.org/journal/PaperInformation.aspx?PaperID=51574#.VG6LFWfHRK0

ABSTRACT

Several epidemiological studies have suggested that carotenoids have antineoplasic activities. The objective of this study was to determine the antiproliferative effect of rich carotenoid lipophilic extracts of papaya fruit pulp ( Carica papaya L., cv Maradol) in breast cancer cells, MCF-7 (estrogen receptor positive) and MDA-MB-231 (estrogen receptor negative), and in non-tumoral mammary epithelial cells MCF-12F. Antiproliferative effect was evaluated using the methylthiazolydiphenyl-tetrazolium bromide (MTT) assay and testing lipophilic extracts from different papaya fruit ripening stages (RS1, RS2, RS3, RS4), at different times (24, 48 and 72 h). Papaya lipophilic extracts do not inhibit cell proliferation of MCF-12F and MDA-MB-231 cells. However, MCF-7 cells showed a significant reduction in proliferation at 72 h with the RS4 papaya extract. Results suggested that lipophilic extracts had different action mechanisms on each type of cells and therefore, more studies were required to elucidate such mechanisms.

Cite this paper

Sancho, L. , Yahia, E. , García-Solís, P. and González-Aguilar, G. (2014) Inhibition of Proliferation of Breast Cancer Cells MCF7 and MDA-MB-231 by Lipophilic Extracts of Papaya (Carica papaya L. var. Maradol) Fruit. Food and Nutrition Sciences, 5, 2097-2103. doi: 10.4236/fns.2014.521222.

References

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http://dx.doi.org/10.1111/j.1541-4337.2004.tb00058.x
[3] Yahia, E.M. (2010) The Contribution of Fruit and Vegetable Consumption to Human Health. In: de la Rosa, L.A., Alvarez-Parrilla, E., Gonzalez-Aguilar, G.A., Eds., Fruit and Vegetable Phytochemicals, Wiley-Blackwell, Iowa, 3-51.
[4] US Department of Agriculture, US Department of Health and Human Services (USDA/HHS) (2010) 2005 Dietary Guidelines Advisory Committee Report.
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[5] Gonzalez-Aguilar, G.A., Celis, J., Sotelo-Mundo, R.R., De La Rosa, L.A., Rodrigo-García, J. and Alvarez-Parrilla, E. (2008) Physiological and Biochemical Changes of Different Fresh-Cut Mango Cultivars Stored at 5?C. International Journal of Food Science and Technology, 43, 91-101.
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[6] McEligot, A.J., Yang, S. and Meyskens Jr., F.L. (2005) Redox Regulation by Intrinsic Species and Extrinsic Nutrients in Normal and Cancer Cells. Annual Review of Nutrition, 25, 261-295.
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[9] Rock, C.L. (2002) Carotenoids and Cervical, Breast, Ovarian, and Colorectal Cancer. Pure and Applied Chemistry, 74, 1451-1459.
http://dx.doi.org/10.1351/pac200274081451
[10] Krinsky, N.I. and Johnson, E.J. (2005) Carotenoid Actions and Their Relation to Health And Disease. Molecular Aspects of Medicine, 26, 459-516.
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[12] Gayosso-García Sancho, L.E., Yahia, E.M., Martínez-Téllez, M.A. and González-Aguilar, G.A. (2010) Effect of Maturity Stage of Papaya Maradol on Physiological and Biochemical Parameters. American Journal of Agricultural and Biological Sciences, 5, 194-203.
http://dx.doi.org/10.3844/ajabssp.2010.194.203
[13] Corral-Aguayo, R.D., Yahia, E.M., Carrillo-Lopez, A. and González-Aguilar, G. (2008) Correlation between Some Nutritional Components and the Total Antioxidant Capacity Measured with Six Different Assays in Eight Horticultural Crops. Journal of Agricultural and Food Chemistry, 56, 10498-10504.
http://dx.doi.org/10.1021/jf801983r
[14] Rivera-Pastrana, D.M., Yahia, E.M. and Gonzalez-Aguilar, G. (2010) Phenolic and Carotenoid Profiles of Papaya Fruit (Carica papaya L.) and Their Contents under Low Temperature Storage. Journal of the Science of Food & Agriculture, 90, 2358-2365.
http://dx.doi.org/10.1002/jsfa.4092
[15] Yahia, E.M., Soto-Zamora, G., Brecht, J.K. and Gardea, A. (2007) Postharvest Hot Air Treatment Effects on the Antioxidant System in Stored Mature-Green Tomatoes. Postharvest Biology and Technology, 44, 107-115.
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[16] Gayosso-García Sancho, L.E., Yahia, E.M. and González-Aguilar, G.A. (2011) Identification and Quantification of Phenols, Carotenoids and Vitamin C from Papaya (Carica Papaya L., cv. Maradol) Fruit Determined by HPLC-DAD-MS/MS ESI. Food Research International, 44, 1284-1291.
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http://dx.doi.org/10.1080/09637480802312922
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[30] Yu, J., Han, W., Kim, J., Lee, J., Ko, E., Kim, E., Moon, H. and Noh, D. (2009) Anoikis-Resistant MDA-MB-231 Cells: Characteristics and Pathway Analysis. Cancer Research, 69, 2062.
http://dx.doi.org/10.1158/0008-5472.SABCS-2062                                                                          eww141121lx

The Centricity Score: A Novel Measurement to Aid in Conservative Breast Cancer Surgery

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http://www.scirp.org/journal/PaperInformation.aspx?PaperID=50713#.VEmmGlfHRK0

ABSTRACT

Introduction: This study describes an intra-operative scoring system to advise the surgeon of the centricity of the tumour in the excised specimen. Methods: Spatial estimations were prospectively made in 10 consecutive patients undergoing wide local excision (WLE) using Bioptics intra-operative digital specimen imaging. The centricity score was defined as 100 – (ICD/SD × 100), where ICD is the inter-centre distance between the specimen’s centre and the tumour’s centre. Results: 10 patients with invasive breast cancer (T1b to T4a), mean age 56 years (range 44 – 71) were studied. The mean tumour and specimen diameter was 24 mm ± 10 (range 12 – 48) and 101 mm ± 22 (range 64 – 140). The mean centricity score was 86 ± 9 (range 65 – 95). Conclusion: This study successfully describes an intraoperative radiological spatial scoring system for patients undergoing WLE. Tumours were well centered in specimens with an overall score of 86/100. The centricity score could be used to guide excision and potentially set benchmarks for conservative breast surgery.

Cite this paper

Sugrue, R. , McGowan, K. , McNamara, C. and Sugrue, M. (2014) The Centricity Score: A Novel Measurement to Aid in Conservative Breast Cancer Surgery. Advances in Breast Cancer Research, 3, 118-123. doi: 10.4236/abcr.2014.34018.

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