McKian Kevin P, Reynolds Carol A, Visscher Daniel W, Nassar Aziza, Radisky Derek C, Vierkant Robert A, Degnim Amy C, Boughey Judy C, Ghosh Karthik, Anderson Stephanie S, Minot Douglas, Caudill Jill L, Vachon Celine M, Frost Marlene H, Pankratz V Shane, Hartmann Lynn C
Department of Oncology, Mayo Clinic Cancer Center, Mayo Graduate School of Medical Education, Mayo Clinic College of Medicine, Rochester, MN 55905, USA.
J Clin Oncol. 2009 Dec 10;27(35):5893-8. doi: 10.1200/JCO.2008.21.5079. Epub 2009 Oct 5.
Accurate, individualized risk prediction for breast cancer is lacking. Tissue-based features may help to stratify women into different risk levels. Breast lobules are the anatomic sites of origin of breast cancer. As women age, these lobular structures should regress, which results in reduced breast cancer risk. However, this does not occur in all women.
We have quantified the extent of lobule regression on a benign breast biopsy in 85 patients who developed breast cancer and 142 age-matched controls from the Mayo Benign Breast Disease Cohort, by determining number of acini per lobule and lobular area. We also calculated Gail model 5-year predicted risks for these women.
There is a step-wise increase in breast cancer risk with increasing numbers of acini per lobule (P = .0004). Adjusting for Gail model score, parity, histology, and family history did not attenuate this association. Lobular area was similarly associated with risk. The Gail model estimates were associated with risk of breast cancer (P = .03). We examined the individual accuracy of these measures using the concordance (c) statistic. The Gail model c statistic was 0.60 (95% CI, 0.50 to 0.70); the acinar count c statistic was 0.65 (95% CI, 0.54 to 0.75). Combining acinar count and lobular area, the c statistic was 0.68 (95% CI, 0.58 to 0.78). Adding the Gail model to these measures did not improve the c statistic.
Novel, tissue-based features that reflect the status of a woman's normal breast lobules are associated with breast cancer risk. These features may offer a novel strategy for risk prediction.
目前缺乏针对乳腺癌的准确、个体化风险预测。基于组织的特征可能有助于将女性分层为不同的风险水平。乳腺小叶是乳腺癌的解剖学起源部位。随着女性年龄增长，这些小叶结构应会退化，从而降低乳腺癌风险。然而，并非所有女性都会出现这种情况。
我们通过确定每个小叶的腺泡数量和小叶面积，对梅奥良性乳腺疾病队列中85例患乳腺癌的患者以及142例年龄匹配的对照者的良性乳腺活检样本中的小叶退化程度进行了量化。我们还计算了这些女性的盖尔模型5年预测风险。
随着每个小叶腺泡数量的增加，乳腺癌风险呈逐步上升趋势（P = 0.0004）。对盖尔模型评分、产次、组织学和家族史进行调整后，这种关联并未减弱。小叶面积与风险也有类似关联。盖尔模型估计值与乳腺癌风险相关（P = 0.03）。我们使用一致性（c）统计量检查了这些指标的个体准确性。盖尔模型的c统计量为0.60（95%可信区间，0.50至0.70）；腺泡计数的c统计量为0.65（95%可信区间，0.54至0.75）。将腺泡计数和小叶面积相结合，c统计量为0.68（95%可信区间，0.58至0.78）。将盖尔模型添加到这些指标中并未改善c统计量。
反映女性正常乳腺小叶状态的基于组织的新特征与乳腺癌风险相关。这些特征可能为风险预测提供一种新策略。
