van de Stolpe Anja, Verhaegh Wim, Blay Jean-Yves, Ma Cynthia X, Pauwels Patrick, Pegram Mark, Prenen Hans, De Ruysscher Dirk, Saba Nabil F, Slovin Susan F, Willard-Gallo Karen, Husain Hatim
Philips MPDx, Eindhoven, Netherlands.
Philips Research, Eindhoven, Netherlands.
Front Genet. 2021 Feb 5;11:598118. doi: 10.3389/fgene.2020.598118. eCollection 2020.
Precision treatment of cancer requires knowledge on active tumor driving signal transduction pathways to select the optimal effective targeted treatment. Currently only a subset of patients derive clinical benefit from mutation based targeted treatment, due to intrinsic and acquired drug resistance mechanisms. Phenotypic assays to identify the tumor driving pathway based on protein analysis are difficult to multiplex on routine pathology samples. In contrast, the transcriptome contains information on signaling pathway activity and can complement genomic analyses. Here we present the validation and clinical application of a new knowledge-based mRNA-based diagnostic assay platform (OncoSignal) for measuring activity of relevant signaling pathways simultaneously and quantitatively with high resolution in tissue samples and circulating tumor cells, specifically with very small specimen quantities. The approach uses mRNA levels of a pathway's direct target genes, selected based on literature for multiple proof points, and used as evidence that a pathway is functionally activated. Using these validated target genes, a Bayesian network model has been built and calibrated on mRNA measurements of samples with known pathway status, which is used next to calculate a pathway activity score on individual test samples. Translation to RT-qPCR assays enables broad clinical diagnostic applications, including small analytes. A large number of cancer samples have been analyzed across a variety of cancer histologies and benchmarked across normal controls. Assays have been used to characterize cell types in the cancer cell microenvironment, including immune cells in which activated and immunotolerant states can be distinguished. Results support the expectation that the assays provide information on cancer driving signaling pathways which is difficult to derive from next generation DNA sequencing analysis. Current clinical oncology applications have been complementary to genomic mutation analysis to improve precision medicine: (1) prediction of response and resistance to various therapies, especially targeted therapy and immunotherapy; (2) assessment and monitoring of therapy efficacy; (3) prediction of invasive cancer cell behavior and prognosis; (4) measurement of circulating tumor cells. Preclinical oncology applications lie in a better understanding of cancer behavior across cancer types, and in development of a pathophysiology-based cancer classification for development of novel therapies and precision medicine.
癌症的精准治疗需要了解活跃的肿瘤驱动信号转导通路，以便选择最佳的有效靶向治疗方法。目前，由于内在和获得性耐药机制，只有一部分患者能从基于突变的靶向治疗中获得临床益处。基于蛋白质分析来识别肿瘤驱动通路的表型分析方法难以在常规病理样本上进行多重检测。相比之下，转录组包含信号通路活性信息，可补充基因组分析。在此，我们展示了一种基于新知识的基于mRNA的诊断检测平台（OncoSignal）的验证及临床应用，该平台能够在组织样本和循环肿瘤细胞中同时、高分辨率且定量地测量相关信号通路的活性，特别是在样本量非常小的情况下。该方法利用基于文献选择的具有多个证据点的通路直接靶基因的mRNA水平，作为通路功能激活的证据。利用这些经过验证的靶基因，已构建了一个贝叶斯网络模型，并根据已知通路状态样本的mRNA测量值进行校准，随后用于计算单个测试样本的通路活性评分。转化为逆转录定量聚合酶链反应（RT-qPCR）检测方法可实现广泛的临床诊断应用，包括微量分析物检测。已经对大量不同癌症组织学类型的癌症样本进行了分析，并与正常对照进行了对比。这些检测方法已用于表征癌细胞微环境中的细胞类型，包括能够区分激活状态和免疫耐受状态的免疫细胞。结果支持了这样的预期，即这些检测方法能够提供难以从下一代DNA测序分析中获得的癌症驱动信号通路信息。当前的临床肿瘤学应用已与基因组突变分析相辅相成，以提高精准医学水平：（1）预测对各种疗法，特别是靶向治疗和免疫治疗的反应及耐药性；（2）评估和监测治疗效果；（3）预测侵袭性癌细胞行为和预后；（4）测量循环肿瘤细胞。临床前肿瘤学应用在于更好地理解不同癌症类型的癌症行为，并为开发新疗法和精准医学建立基于病理生理学的癌症分类。
