Haider Syed, Tyekucheva Svitlana, Prandi Davide, Fox Natalie S, Ahn Jaeil, Xu Andrew Wei, Pantazi Angeliki, Park Peter J, Laird Peter W, Sander Chris, Wang Wenyi, Demichelis Francesca, Loda Massimo, Boutros Paul C
Ontario Institute for Cancer Research, Toronto, Ontario, Canada.
The Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London, United Kingdom.
JCO Precis Oncol. 2020 Sep 4;4. doi: 10.1200/PO.20.00016. eCollection 2020.
The tumor microenvironment is complex, comprising heterogeneous cellular populations. As molecular profiles are frequently generated using bulk tissue sections, they represent an admixture of multiple cell types (including immune, stromal, and cancer cells) interacting with each other. Therefore, these molecular profiles are confounded by signals emanating from many cell types. Accurate assessment of residual cancer cell fraction is crucial for parameterization and interpretation of genomic analyses, as well as for accurately interpreting the clinical properties of the tumor.
To benchmark cancer cell fraction estimation methods, 10 estimators were applied to a clinical cohort of 333 patients with prostate cancer. These methods include gold-standard multiobserver pathology estimates, as well as estimates inferred from genome, epigenome, and transcriptome data. In addition, two methods based on genomic and transcriptomic profiles were used to quantify tumor purity in 4,497 tumors across 12 cancer types. Bulk mRNA and microRNA profiles were subject to in silico deconvolution to estimate cancer cell-specific mRNA and microRNA profiles.
We present a systematic comparison of 10 tumor purity estimation methods on a cohort of 333 prostate tumors. We quantify variation among purity estimation methods and demonstrate how this influences interpretation of clinico-genomic analyses. Our data show poor concordance between pathologic and molecular purity estimates, necessitating caution when interpreting molecular results. Limited concordance between DNA- and mRNA-derived purity estimates remained a general pan-cancer phenomenon when tested in an additional 4,497 tumors spanning 12 cancer types.
The choice of tumor purity estimation method may have a profound impact on the interpretation of genomic assays. Taken together, these data highlight the need for improved assessment of tumor purity and quantitation of its influences on the molecular hallmarks of cancers.
肿瘤微环境复杂,由异质性细胞群体组成。由于分子谱通常是使用大体组织切片生成的,它们代表了多种相互作用的细胞类型(包括免疫细胞、基质细胞和癌细胞)的混合物。因此,这些分子谱被来自多种细胞类型的信号所混淆。准确评估残留癌细胞分数对于基因组分析的参数化和解释,以及准确解读肿瘤的临床特性至关重要。
为了对癌细胞分数估计方法进行基准测试,将10种估计方法应用于333例前列腺癌患者的临床队列。这些方法包括金标准多观察者病理学估计,以及从基因组、表观基因组和转录组数据推断出的估计。此外,基于基因组和转录组谱的两种方法被用于量化12种癌症类型的4,497个肿瘤中的肿瘤纯度。对大量mRNA和微小RNA谱进行计算机反卷积,以估计癌细胞特异性mRNA和微小RNA谱。
我们对333例前列腺肿瘤队列中的10种肿瘤纯度估计方法进行了系统比较。我们量化了纯度估计方法之间的差异,并展示了这如何影响临床基因组分析的解释。我们的数据显示病理和分子纯度估计之间的一致性较差,在解释分子结果时需要谨慎。当在另外4,497个跨越12种癌症类型的肿瘤中进行测试时,DNA和mRNA衍生的纯度估计之间的有限一致性仍然是一种普遍的泛癌现象。
肿瘤纯度估计方法的选择可能对基因组检测的解释产生深远影响。综上所述,这些数据凸显了改进肿瘤纯度评估及其对癌症分子特征影响定量的必要性。