Ali H Raza, Dariush Aliakbar, Provenzano Elena, Bardwell Helen, Abraham Jean E, Iddawela Mahesh, Vallier Anne-Laure, Hiller Louise, Dunn Janet A, Bowden Sarah J, Hickish Tamas, McAdam Karen, Houston Stephen, Irwin Mike J, Pharoah Paul D P, Brenton James D, Walton Nicholas A, Earl Helena M, Caldas Carlos
Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Cambridge, UK.
Department of Pathology, University of Cambridge, Cambridge, UK.
Breast Cancer Res. 2016 Feb 16;18(1):21. doi: 10.1186/s13058-016-0682-8.
There is a need to improve prediction of response to chemotherapy in breast cancer in order to improve clinical management and this may be achieved by harnessing computational metrics of tissue pathology. We investigated the association between quantitative image metrics derived from computational analysis of digital pathology slides and response to chemotherapy in women with breast cancer who received neoadjuvant chemotherapy.
We digitised tissue sections of both diagnostic and surgical samples of breast tumours from 768 patients enrolled in the Neo-tAnGo randomized controlled trial. We subjected digital images to systematic analysis optimised for detection of single cells. Machine-learning methods were used to classify cells as cancer, stromal or lymphocyte and we computed estimates of absolute numbers, relative fractions and cell densities using these data. Pathological complete response (pCR), a histological indicator of chemotherapy response, was the primary endpoint. Fifteen image metrics were tested for their association with pCR using univariate and multivariate logistic regression.
Median lymphocyte density proved most strongly associated with pCR on univariate analysis (OR 4.46, 95 % CI 2.34-8.50, p < 0.0001; observations = 614) and on multivariate analysis (OR 2.42, 95 % CI 1.08-5.40, p = 0.03; observations = 406) after adjustment for clinical factors. Further exploratory analyses revealed that in approximately one quarter of cases there was an increase in lymphocyte density in the tumour removed at surgery compared to diagnostic biopsies. A reduction in lymphocyte density at surgery was strongly associated with pCR (OR 0.28, 95 % CI 0.17-0.47, p < 0.0001; observations = 553).
A data-driven analysis of computational pathology reveals lymphocyte density as an independent predictor of pCR. Paradoxically an increase in lymphocyte density, following exposure to chemotherapy, is associated with a lack of pCR. Computational pathology can provide objective, quantitative and reproducible tissue metrics and represents a viable means of outcome prediction in breast cancer.
ClinicalTrials.gov NCT00070278 ; 03/10/2003.
为改善乳腺癌化疗反应的预测以优化临床管理，可借助组织病理学的计算指标来实现。我们研究了从数字病理切片的计算分析中得出的定量图像指标与接受新辅助化疗的乳腺癌女性化疗反应之间的关联。
我们将参与Neo-tAnGo随机对照试验的768例患者的乳腺肿瘤诊断和手术样本的组织切片进行数字化处理。我们对数字图像进行系统分析，优化用于单细胞检测。使用机器学习方法将细胞分类为癌症细胞、基质细胞或淋巴细胞，并利用这些数据计算绝对数量、相对比例和细胞密度的估计值。病理完全缓解（pCR）作为化疗反应的组织学指标，是主要终点。使用单变量和多变量逻辑回归测试了15个图像指标与pCR的关联。
在单变量分析中，淋巴细胞密度中位数与pCR的相关性最强（OR 4.46，95%CI 2.34 - 8.50，p < 0.0001；观察值 = 614），在调整临床因素后的多变量分析中也是如此（OR 2.42，95%CI 1.08 - 5.40，p = 0.03；观察值 = 406）。进一步的探索性分析显示，在大约四分之一的病例中，与诊断性活检相比，手术切除肿瘤中的淋巴细胞密度增加。手术时淋巴细胞密度降低与pCR密切相关（OR 0.28，95%CI 0.17 - 0.47，p < 0.0001；观察值 = 553）。
对计算病理学进行数据驱动分析发现，淋巴细胞密度是pCR的独立预测因子。矛盾的是，化疗后淋巴细胞密度增加与未达到pCR相关。计算病理学能够提供客观、定量且可重复的组织指标，是乳腺癌预后预测的一种可行方法。
ClinicalTrials.gov NCT00070278；2003年10月3日。
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