Department of Data Sciences, Dana-Farber Cancer Institute, Boston, Massachusetts.
Biostatistics, CUNY Graduate School of Public Health and Health Policy, New York, New York.
Cancer Epidemiol Biomarkers Prev. 2020 Feb;29(2):509-519. doi: 10.1158/1055-9965.EPI-18-1359. Epub 2019 Dec 23.
Recent efforts to improve outcomes for high-grade serous ovarian cancer, a leading cause of cancer death in women, have focused on identifying molecular subtypes and prognostic gene signatures, but existing subtypes have poor cross-study robustness. We tested the contribution of cell admixture in published ovarian cancer molecular subtypes and prognostic gene signatures.
Gene signatures of tumor and stroma were developed using paired microdissected tissue from two independent studies. Stromal genes were investigated in two molecular subtype classifications and 61 published gene signatures. Prognostic performance of gene signatures of stromal admixture was evaluated in 2,527 ovarian tumors (16 studies). Computational simulations of increasing stromal cell proportion were performed by mixing gene-expression profiles of paired microdissected ovarian tumor and stroma.
Recently described ovarian cancer molecular subtypes are strongly associated with the cell admixture. Tumors were classified as different molecular subtypes in simulations where the percentage of stromal cells increased. Stromal gene expression in bulk tumors was associated with overall survival (hazard ratio, 1.17; 95% confidence interval, 1.11-1.23), and in one data set, increased stroma was associated with anatomic sampling location. Five published prognostic gene signatures were no longer prognostic in a multivariate model that adjusted for stromal content.
Cell admixture affects the interpretation and reproduction of ovarian cancer molecular subtypes and gene signatures derived from bulk tissue. Elucidating the role of stroma in the tumor microenvironment and in prognosis is important.
Single-cell analyses may be required to refine the molecular subtypes of high-grade serous ovarian cancer.
最近,为了提高高级别浆液性卵巢癌(女性癌症死亡的主要原因)的治疗效果,人们致力于识别分子亚型和预后基因特征,但现有的亚型在跨研究中稳定性较差。我们检验了细胞混杂对已发表的卵巢癌分子亚型和预后基因特征的影响。
使用来自两个独立研究的配对显微解剖组织,开发了肿瘤和基质的基因特征。在两个分子亚型分类和 61 个已发表的基因特征中研究了基质基因。对 2527 个卵巢肿瘤(16 项研究)中基质混杂基因特征的预后性能进行了评估。通过混合配对显微解剖卵巢肿瘤和基质的基因表达谱,进行了基质细胞比例增加的计算模拟。
最近描述的卵巢癌分子亚型与细胞混杂密切相关。在模拟中,随着基质细胞比例的增加,肿瘤被分类为不同的分子亚型。在大量肿瘤中,基质基因表达与总生存期相关(风险比,1.17;95%置信区间,1.11-1.23),在一个数据集,增加的基质与解剖采样位置相关。在调整了基质含量的多变量模型中,五个已发表的预后基因特征不再具有预后意义。
细胞混杂影响了从大量组织中获得的卵巢癌分子亚型和基因特征的解释和再现。阐明基质在肿瘤微环境和预后中的作用非常重要。
可能需要单细胞分析来细化高级别浆液性卵巢癌的分子亚型。