天文学算法在乳腺癌组织蛋白表达自动分析中的应用。

BACKGROUND

High-throughput evaluation of tissue biomarkers in oncology has been greatly accelerated by the widespread use of tissue microarrays (TMAs) and immunohistochemistry. Although TMAs have the potential to facilitate protein expression profiling on a scale to rival experiments of tumour transcriptomes, the bottleneck and imprecision of manually scoring TMAs has impeded progress.

METHODS

We report image analysis algorithms adapted from astronomy for the precise automated analysis of IHC in all subcellular compartments. The power of this technique is demonstrated using over 2000 breast tumours and comparing quantitative automated scores against manual assessment by pathologists.

RESULTS

All continuous automated scores showed good correlation with their corresponding ordinal manual scores. For oestrogen receptor (ER), the correlation was 0.82, P<0.0001, for BCL2 0.72, P<0.0001 and for HER2 0.62, P<0.0001. Automated scores showed excellent concordance with manual scores for the unsupervised assignment of cases to 'positive' or 'negative' categories with agreement rates of up to 96%.

CONCLUSION

The adaptation of astronomical algorithms coupled with their application to large annotated study cohorts, constitutes a powerful tool for the realisation of the enormous potential of digital pathology.

背景

组织微阵列（TMA）和免疫组织化学的广泛应用极大地加速了肿瘤学中组织生物标志物的高通量评估。尽管 TMA 有可能在规模上促进蛋白质表达谱分析，可与肿瘤转录组实验相媲美，但手动评分 TMA 的瓶颈和不精确性阻碍了进展。

方法

我们报告了从天文学中改编的图像分析算法，用于精确自动分析所有亚细胞区室中的免疫组织化学。通过比较超过 2000 例乳腺癌的定量自动评分与病理学家的手动评估，证明了该技术的强大功能。

结果

所有连续的自动评分都与它们对应的有序手动评分具有良好的相关性。对于雌激素受体（ER），相关性为 0.82，P<0.0001，对于 BCL2 为 0.72，P<0.0001，对于 HER2 为 0.62，P<0.0001。自动评分与手动评分在对病例进行“阳性”或“阴性”分类的无监督分配方面具有极好的一致性，一致性率高达 96%。