Tothill Richard W, Shi Fan, Paiman Lisa, Bedo Justin, Kowalczyk Adam, Mileshkin Linda, Buela Evangeline, Klupacs Robert, Bowtell David, Byron Keith
1Peter MacCallum Cancer Centre, East Melbourne 2National (ICT) Australia, The University of Melbourne, Parkville 3Healthscope Pathology, Clayton 4Circadian Technologies Limited, Toorak 5The Sir Peter MacCallum Department of Oncology, University of Melbourne, Parkville 6The Department of Pathology, University of Melbourne, Parkville 7The Department of Biochemistry, University of Melbourne, Parkville, Vic, Australia.
Pathology. 2015 Jan;47(1):7-12. doi: 10.1097/PAT.0000000000000194.
Accurate identification of the primary tumour in cancer of unknown primary (CUP) is required for effective treatment selection and improved patient outcomes. The aim of this study was to develop and validate a gene expression tumour classifier and integrate it with histopathology to identify the likely site of origin in CUP.RNA was extracted from 450 formalin fixed, paraffin embedded samples of known origin comprising 18 tumour groups. Whole genome expression analysis was performed using a bead-based array. Classification of the tumours made use of a binary support vector machine, together with recursive feature elimination. A hierarchical tumour classifier was developed and incorporated with conventional histopathology to identify the origins of metastatic tumours.The classifier demonstrated an accuracy of 88% for correctly predicting the tumour type on a validation set of known tumours (n = 94). For CUP samples (n = 49) having a final clinical diagnosis, the classifier improved the accuracy of histology alone for both single and multiple predictions. Furthermore, where histology alone could not suggest any specific diagnosis, the classifier was able to correctly predict the primary site of origin.We demonstrate the integration of gene expression profiling with conventional histopathology to aid the investigation of CUP.
准确识别原发性不明癌症（CUP）中的原发肿瘤对于有效选择治疗方案和改善患者预后至关重要。本研究的目的是开发并验证一种基因表达肿瘤分类器，并将其与组织病理学相结合，以确定CUP中可能的肿瘤起源部位。从450个已知起源的福尔马林固定、石蜡包埋样本中提取RNA，这些样本包括18个肿瘤组。使用基于微珠的芯片进行全基因组表达分析。肿瘤分类采用二元支持向量机，并结合递归特征消除。开发了一种分层肿瘤分类器，并将其与传统组织病理学相结合，以确定转移性肿瘤的起源。该分类器在一组已知肿瘤的验证集（n = 94）上对肿瘤类型进行正确预测的准确率为88%。对于具有最终临床诊断的CUP样本（n = 49），该分类器提高了单纯组织学对单个和多个预测的准确性。此外，在单纯组织学无法给出任何具体诊断的情况下，该分类器能够正确预测原发起源部位。我们展示了基因表达谱与传统组织病理学的整合，以辅助CUP的研究。
