The Breast and Ovarian Cancer Risks in Korea Due to Inherited Mutations in BRCA1 and BRCA2: A Preliminary Report

본 논문은 2007 Global Breast Cancer Conference (GBCC)에서 포스터 발표되었음.Purpose: To estimate the cumulative risk till each age (penetrance) of breast and ovarian cancers among female family members with BRCA1 and BRCA2 mutation. Methods: Among the 61 BRCA1 mutation carriers in the 42 families and 47 BRCA2 mutation carriers in 31 families identified at 5 academic breast clinics, the probands were excluded to estimate the cumulative risk till each age of breast cancer in the Korean BRCA1 and BRCA2 carriers. Using Kaplan-Meier analyses, cumulative cancer risk estimates were determined. Results: By the age 70, the female breast cancer risk for the BRCA1 and BRCA2 mutation carriers was 72.1% (95% confidence interval [CI]=59.5% to 84.8%) and 66.3% (95% CI=41.2% to 91.5%), respectively, and the ovarian cancer risk was 24.6% (95% CI=0% to 50.3%) and 11.1% (95% CI=0% to 31.6%), respectively. The contralateral breast cancer risk at 5 years after primary breast cancer was estimated as 16.2% (95% CI=9.3% to 23.1%) for the 52 breast cancer patients with the BRCA1 mutation and 17.3% (95% CI=9.7% to 24.0%) for the 35 breast cancer patients with the BRCA2 mutation. Conclusion: The penetrance of BRCA mutations in Korea is largely consistent with the previous studies on Western populations. However, the small number of the cases, the high proportions of probands in the study subjects, the short term follow-up, and large confidence intervals are the limitations of the current study. The Korean Hereditary Breast Cancer Study (KOHBRA Study) may definitely answer this question.Kim KS, 2008, J BREAST CANCER, V11, P95Kim EK, 2007, J BREAST CANCER, V10, P241Vogl FD, 2007, FAM CANCER, V6, P63, DOI 10.1007/s10689-006-9106-8Ahn SH, 2007, CANCER LETT, V245, P90, DOI 10.1016/j.canlet.2005.12.031Schlich-Bakker KJ, 2006, PATIENT EDUC COUNS, V62, P13, DOI 10.1016/j.pec.2005.08.012Metcalfe K, 2004, J CLIN ONCOL, V22, P2328, DOI 10.1200/JCO.2004.04.033Choi DH, 2004, J CLIN ONCOL, V22, P1638, DOI 10.1200/JCO.2004.04.179King MC, 2003, SCIENCE, V302, P643Antoniou A, 2003, AM J HUM GENET, V72, P1117Kauff ND, 2003, CANCER, V97, P1601, DOI 10.1002/cncr.11225Liede A, 2002, HUM MUTAT, V20, P413, DOI 10.1002/humu.10154Brose MS, 2002, J NATL CANCER I, V94, P1365IKEDA N, 2002, J BREAST CANCER, V5, P194Eng C, 2001, J MED GENET, V38, P824Risch HA, 2001, AM J HUM GENET, V68, P700ROBSON ME, 2001, CURR PROB SURG, V38, P387Ponder BAJ, 2000, BRIT J CANCER, V83, P1301Matloff ET, 2000, J CLIN ONCOL, V18, P2484Wagner TMU, 2000, BRIT J CANCER, V82, P1249Julian-Reynier C, 2000, EUR J HUM GENET, V8, P204Schrag D, 2000, JAMA-J AM MED ASSOC, V283, P617Antoniou AC, 2000, GENET EPIDEMIOL, V18, P173Arnold N, 1999, HUM MUTAT, V14, P333Newman B, 1998, JAMA-J AM MED ASSOC, V279, P915Ford D, 1998, AM J HUM GENET, V62, P676Claus EB, 1996, CANCER, V77, P2318Nieto FJ, 1996, AM J EPIDEMIOL, V143, P1059FORD D, 1995, AM J HUM GENET, V57, P1457EASTON DF, 1995, AM J HUM GENET, V56, P265FORD D, 1994, LANCET, V343, P692KAPLAN EL, 1958, J AM STAT ASSOC, V53, P457

A study on signal-to-noise ratios and power Transformations

제품설계 또는 공정설계를 위한 모수설계의 목적은 잡음의 영향하에서도 성능특성치의 분산이 작고, 평균이 목표치에 근접하도록 하는 설계변수의 최적 조건을 찾는 것이다. 본 논문에서는 모수설계를 위한 모수선택기준으로서 다구찌박사가 제안한 '신호 대 잡음의 비'를 분산의 척도로 이용하는 2단계 모수선택법과, 자료에 근거한 접근방법으로 변환에의하여 모수를 선택하는 방법을 비교연구하였다. 2 장에서는 다구찌박사의 신호 대 잡음의 비를 적용한 모수선택과정율 설명하고, 이 기준을 일반화시킨 개념인 'PerMIA'를 소개하였다. 산포의 척도인 PerMIA의 활용은 분산과 평균의 의존성에 기인하는 산포효과의 제거에 착안한다. 3 장에서는 이개념의 연장으로, 분산과 평군의 독립성을 획득하기위하여 적절한 변환을 활용하는 모수선택과정을 제시하였다. 4 장에서는 모의실험을 통해, 적절한 변환을 선택하기위한 기법을 예시하고, 합성하여 산출된 자료들을 본 논문에서 제시한 두가지 접근방법에의하여 분석하여 모수선택을 실행한 결과를 비교하였다.CONTENTS = ⅰ List of Tables = ⅱ List of Figures = ⅲ CHAPTER 1. Introduction = 1 CHAPTER 2. Signal-to-Noise Ratio and Performance Measurce Independent of Adjustment = 4 2.1 Taguchi's Approach to Parameter Design = 4 2.2 optimization Procedure Using SN-ratio = 6 2.3 Extension of SN-ratio to PerMIA = 8 CHAPTER 3. Analysis of Transformation = 11 3.1 Log Transformation = 11 3.2 General Transformation of y = 13 CHAPTER 4. Simulations = 15 4.1 Determining a Transformation = 15 4.2 Selection of Design Parameters : Taguchi's SN-ratio v.s Power Transformations = 23 REFERENCES = 29 APPENDIX = 30 논문초록 = 3