Authors' Affiliations: Departments of Medical Oncology and Biostatistics and Computational Biology, Dana-Farber Cancer Institute; Department of Medicine, Brigham and Women's Hospital; Department of Medicine, Harvard Medical School; Department of Biostatistics, Harvard School of Public Health, Boston; Departments of Physics and Biology, Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology; Harvard Stem Cell Institute, Cambridge, Massachusetts; Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, New York, New York; Departments of Pathology and Oncology, Johns Hopkins University School of Medicine, Baltimore, Maryland; Department of Medical Oncology, Hospital Clinic, Institut d'Investigacions Biomediques August Pi i Sunyer, Barcelona, Spain; Department of Pathology, MizMedi Hospital, Seoul, South Korea; Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel; and Hubrecht Institute, Royal Netherlands Academy of Arts and Sciences and University Medical Center Utrecht, Utrecht, the Netherlands.
Cancer Res. 2014 Mar 1;74(5):1338-48. doi: 10.1158/0008-5472.CAN-13-2357-T. Epub 2014 Jan 21.
Metastatic disease is the main cause of cancer-related mortality due to almost universal therapeutic resistance. Despite its high clinical relevance, our knowledge of how cancer cell populations change during metastatic progression is limited. Here, we investigated intratumor genetic and phenotypic heterogeneity during metastatic progression of breast cancer. We analyzed cellular genotypes and phenotypes at the single cell level by performing immunoFISH in intact tissue sections of distant metastatic tumors from rapid autopsy cases and from primary tumors and matched lymph node metastases collected before systemic therapy. We calculated the Shannon index of intratumor diversity in all cancer cells and within phenotypically distinct cell populations. We found that the extent of intratumor genetic diversity was similar regardless of the chromosomal region analyzed, implying that it may reflect an inherent property of the tumors. We observed that genetic diversity was highest in distant metastases and was generally concordant across lesions within the same patient, whereas treatment-naïve primary tumors and matched lymph node metastases were frequently genetically more divergent. In contrast, cellular phenotypes were more discordant between distant metastases than primary tumors and matched lymph node metastases. Diversity for 8q24 was consistently higher in HER2(+) tumors compared with other subtypes and in metastases of triple-negative tumors relative to primary sites. We conclude that our integrative method that couples ecologic models with experimental data in human tissue samples could be used for the improved prognostication of patients with cancer and for the design of more effective therapies for progressive disease.
转移性疾病是癌症相关死亡的主要原因，这主要是由于几乎普遍存在的治疗耐药性。尽管它具有很高的临床相关性，但我们对癌细胞群体在转移性进展过程中如何变化的了解是有限的。在这里，我们研究了乳腺癌转移性进展过程中的肿瘤内遗传和表型异质性。我们通过对快速尸检病例和原发性肿瘤以及在系统性治疗前收集的匹配淋巴结转移灶的完整组织切片进行免疫荧光原位杂交，在单细胞水平上分析了细胞基因型和表型。我们计算了所有癌细胞和表型不同的细胞群体内肿瘤内多样性的香农指数。我们发现，肿瘤内遗传多样性的程度与所分析的染色体区域无关，这表明它可能反映了肿瘤的固有特性。我们观察到，遗传多样性在远处转移中最高，并且通常在同一患者的病变之间一致，而未经治疗的原发性肿瘤和匹配的淋巴结转移灶通常在遗传上更具差异性。相比之下，远处转移中的细胞表型比原发性肿瘤和匹配的淋巴结转移灶更不一致。与其他亚型相比，HER2(+)肿瘤中 8q24 的多样性始终更高，与原发性肿瘤相比，三阴性肿瘤的转移灶中的多样性更高。我们得出的结论是，我们的方法将生态模型与人类组织样本中的实验数据相结合，可以用于改善癌症患者的预后，并为进展性疾病设计更有效的治疗方法。
