Sammut Stephen-John, Crispin-Ortuzar Mireia, Chin Suet-Feung, Provenzano Elena, Bardwell Helen A, Ma Wenxin, Cope Wei, Dariush Ali, Dawson Sarah-Jane, Abraham Jean E, Dunn Janet, Hiller Louise, Thomas Jeremy, Cameron David A, Bartlett John M S, Hayward Larry, Pharoah Paul D, Markowetz Florian, Rueda Oscar M, Earl Helena M, Caldas Carlos
Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Cambridge, UK.
Department of Oncology, University of Cambridge, Cambridge, UK.
Nature. 2022 Jan;601(7894):623-629. doi: 10.1038/s41586-021-04278-5. Epub 2021 Dec 7.
Breast cancers are complex ecosystems of malignant cells and the tumour microenvironment. The composition of these tumour ecosystems and interactions within them contribute to responses to cytotoxic therapy. Efforts to build response predictors have not incorporated this knowledge. We collected clinical, digital pathology, genomic and transcriptomic profiles of pre-treatment biopsies of breast tumours from 168 patients treated with chemotherapy with or without HER2 (encoded by ERBB2)-targeted therapy before surgery. Pathology end points (complete response or residual disease) at surgery were then correlated with multi-omic features in these diagnostic biopsies. Here we show that response to treatment is modulated by the pre-treated tumour ecosystem, and its multi-omics landscape can be integrated in predictive models using machine learning. The degree of residual disease following therapy is monotonically associated with pre-therapy features, including tumour mutational and copy number landscapes, tumour proliferation, immune infiltration and T cell dysfunction and exclusion. Combining these features into a multi-omic machine learning model predicted a pathological complete response in an external validation cohort (75 patients) with an area under the curve of 0.87. In conclusion, response to therapy is determined by the baseline characteristics of the totality of the tumour ecosystem captured through data integration and machine learning. This approach could be used to develop predictors for other cancers.
乳腺癌是由恶性细胞和肿瘤微环境构成的复杂生态系统。这些肿瘤生态系统的组成及其内部的相互作用会影响细胞毒性疗法的疗效。构建反应预测指标的研究尚未纳入这一知识。我们收集了168例在手术前接受化疗（有或没有HER2（由ERBB2编码）靶向治疗）的乳腺癌患者治疗前活检的临床、数字病理学、基因组和转录组图谱。然后将手术时的病理学终点（完全缓解或残留疾病）与这些诊断性活检中的多组学特征进行关联。在此我们表明，治疗反应受治疗前肿瘤生态系统的调节，并且其多组学格局可通过机器学习整合到预测模型中。治疗后残留疾病的程度与治疗前特征呈单调相关，这些特征包括肿瘤突变和拷贝数格局、肿瘤增殖、免疫浸润以及T细胞功能障碍和排除。将这些特征整合到一个多组学机器学习模型中，在一个外部验证队列（75例患者）中预测病理完全缓解的曲线下面积为0.87。总之，治疗反应由通过数据整合和机器学习捕获的整个肿瘤生态系统的基线特征决定。这种方法可用于开发其他癌症的预测指标。
