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Cross-species analysis of genetically engineered mouse models of MAPK-driven colorectal cancer identifies hallmarks of the human disease

机译:MAPK驱动的大肠癌基因工程小鼠模型的跨物种分析确定了人类疾病的标志

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Effective treatment options for advanced colorectal cancer (CRC) are limited, survival rates are poor and this disease continues to be a leading cause of cancer-related deaths worldwide. Despite being a highly heterogeneous disease, a large subset of individuals with sporadic CRC typically harbor relatively few established ‘driver’ lesions. Here, we describe a collection of genetically engineered mouse models (GEMMs) of sporadic CRC that combine lesions frequently altered in human patients, including well-characterized tumor suppressors and activators of MAPK signaling. Primary tumors from these models were profiled, and individual GEMM tumors segregated into groups based on their genotypes. Unique allelic and genotypic expression signatures were generated from these GEMMs and applied to clinically annotated human CRC patient samples. We provide evidence that a Kras signature derived from these GEMMs is capable of distinguishing human tumors harboring KRAS mutation, and tracks with poor prognosis in two independent human patient cohorts. Furthermore, the analysis of a panel of human CRC cell lines suggests that high expression of the GEMM Kras signature correlates with sensitivity to targeted pathway inhibitors. Together, these findings implicate GEMMs as powerful preclinical tools with the capacity to recapitulate relevant human disease biology, and support the use of genetic signatures generated in these models to facilitate future drug discovery and validation efforts.
机译:晚期大肠癌(CRC)的有效治疗选择有限,存活率低,并且该疾病继续成为全球范围内与癌症相关的死亡的主要原因。尽管是高度异质性疾病,但散发性CRC的大部分个体通常都携带相对较少的既定“驾驶员”病变。在这里,我们描述了散发性CRC的基因工程小鼠模型(GEMM)的集合,这些模型结合了人类患者中经常发生的病变,包括特征明确的肿瘤抑制因子和MAPK信号激活因子。对来自这些模型的原发肿瘤进行了分析,并根据基因型将单个GEMM肿瘤分为几类。从这些GEMM生成独特的等位基因和基因型表达签名,并将其应用于临床注释的人类CRC患者样品。我们提供的证据表明,源自这些GEMM的Kras签名能够区分具有KRAS突变的人类肿瘤,并在两个独立的人类患者队列中追踪预后不良。此外,对一组人类CRC细胞系的分析表明,GEMM Kras标记的高表达与对靶向途径抑制剂的敏感性相关。总之,这些发现暗示GEMMs是强大的临床前工具,具有概括相关人类疾病生物学的能力,并支持使用这些模型中产生的遗传特征来促进未来的药物发现和验证工作。

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