乳腺癌病理诊断系列转录组定性标志的构建与应用

The molecular pathological diagnosis of breast cancer, which is the basis of treatment for patients, relies heavily on technical issues such as tissue fixation, batches of antibodies and reagents, operative technique and personal interpretation of pathologists and therefore lacks veracity and timeliness. The quantitative signatures developed based on the absolute gene expression measured values are often unfit for clinical application as a result of batch effects of measurement, the variations of proportions of tumor epithelial cell in clinical tissues samples, the partial RNA degradation and amplification bias of low-input RNA. However, we have found the qualitative transcriptional features, the within-sample relative expression orderings (REOs), are highly robust against the above factors, which make it a promising approach for developing robust qualitative signatures, and have developed an auxiliary qualitative signature to reclassify ER status for breast cancer patients in our previous researches. In this project, we will ①optimize the ER signature and develop qualitative signatures for predictions of PR, HER2, Ki67 status and histological grade in breast cancer;②collect fresh-frozen tissues specimens obtained by needle biopsy and fresh-frozen and formalin-fixed paraffin-embedded tissues specimens obtained by surgery resection from breast cancer patients to demonstrate the clinical applicability of the five qualitative signatures;③classify breast cancer patients into five subtypes (Luminal A, Luminal B-HER2 negative, Luminal B-HER2 positive, HER2 overexpression and triple-negative) using the five signatures and demonstrate the higher correlation between the reclassified subtypes and clinical prognosis;④identify the subtype-specific molecular mechanism using multi-omics data. The five qualitative signatures for pathological diagnosis can be tested together through single RNA-sequencing, thus are more clinically applicable and timely.

分子病理诊断结果是制订乳腺癌治疗方案的依据，但样本固定、试剂批次、操作技术、病理学家经验等因素影响其准确性与时效性。基于基因表达值打分的转录组定量标志受检测批次效应、癌细胞占比变化、RNA部分降解、微量RNA扩增偏倚等影响，缺乏临床适用性。我们前期发现转录组定性特征（样本内基因表达值相对大小秩序关系）对上述因素不敏感，据此构建了乳腺癌ER状态辅助鉴别标志。本课题拟①优化该ER标志，并构建PR、HER2、Ki67和组织学分级标志；②收集穿刺活检的冷冻组织、手术切除的冷冻和石蜡包埋组织样本证实这五组标志的临床适用性；③整合五组标志将乳腺癌分为管腔A、管腔B-HER2阴、管腔B-HER2阳、HER2过表达和三阴性五种亚型，证实基于定性标志的分型与临床预后具有更高的相关性；④识别亚型特异的多维组学特征和分子机制。本课题构建的五组病理诊断标志可通过单次微量RNA测序完成检测，更具临床适用性与时效性。

乳腺癌是女性中发病率和死亡率最高的恶性肿瘤，具有高度异质性。临床治疗过程中患者的准确分类，对指导临床用药至关重要。前期研究发现基于样本内基因表达相对大小秩序关系的转录组定性标志具有高度稳健性，可用于个体化判断，更加适用于临床。我们基于转录组定性特征分别构建了①可鉴别HG1和HG3患者和可对病理学诊断的HG2乳腺癌患者进行重新分类的组织分级标志；②可鉴别ER、PR、HER2和Ki67状态的分子标志，③ER阴性乳腺癌新辅助化疗极限耐药标志。另一方面我们还改进了基于转录组定性特征构建的个体化差异表达基因识别算法，使其有更高的统计效能和计算效率。将该算法应用于三阴性乳腺癌患者的表达谱数据，我们从共同失调基因中识别出16个三阴性乳腺癌潜在的药靶基因，这些基因与已获批的药物或抗肿瘤药物都有相互作用；基于高度变异基因，我们识别出三阴性乳腺癌中三个预后不同的亚型。最后，基于个体化差异表达基因分析方法，通过整合基因组学、表观基因组学、转录组学和蛋白互作用组学数据，我们识别了结直肠癌亚型的235个功能性基因调控子，相同的功能性调控子在不同的亚型中调控机制不同。基于转录组定性特征优化的结直肠癌亚型分类器避免了样本组成对分类结果的影响。本项目基于转录组定性特征构建的标志能更加客观、准确地为患者提供预后和药效预测信息，具有重要的临床转化意义。个体化差异表达基因的识别为肿瘤的异质性研究和挖掘潜在药物靶标提供了新的思路。