Department of Cardiovascular and Metabolic Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, 44195, USA.
Robert J. Tomsich Pathology and Laboratory Medicine Institute, Cleveland Clinic, Cleveland, OH, 44195, USA.
Virchows Arch. 2024 Feb;484(2):369-375. doi: 10.1007/s00428-023-03708-1. Epub 2023 Nov 24.
Cancer of unknown primary (CUP) presents a complex diagnostic challenge, characterized by metastatic tumors of unknown tissue origin and a dismal prognosis. This review delves into the emerging significance of artificial intelligence (AI) and machine learning (ML) in transforming the landscape of CUP diagnosis, classification, and treatment. ML approaches, trained on extensive molecular profiling data, have shown promise in accurately predicting tissue of origin. Genomic profiling, encompassing driver mutations and copy number variations, plays a pivotal role in CUP diagnosis by providing insights into tumor type-specific oncogenic alterations. Mutational signatures (MS), reflecting somatic mutation patterns, offer further insights into CUP diagnosis. Known MS with established etiology, such as ultraviolet (UV) light-induced DNA damage and tobacco exposure, have been identified in cases of dedifferentiated/transdifferentiated melanoma and carcinoma. Deep learning models that integrate gene expression data and DNA methylation patterns offer insights into tissue lineage and tumor classification. In digital pathology, machine learning algorithms analyze whole-slide images to aid in CUP classification. Finally, precision oncology, guided by molecular profiling, offers targeted therapies independent of primary tissue identification. Clinical trials assigning CUP patients to molecularly guided therapies, including targetable alterations and tumor mutation burden as an immunotherapy biomarker, have resulted in improved overall survival in a subset of patients. In conclusion, AI- and ML-driven approaches are revolutionizing CUP management by enhancing diagnostic accuracy. Precision oncology utilizing enhanced molecular profiling facilitates the identification of targeted therapies that transcend the need to identify the tissue of origin, ultimately improving patient outcomes.
原发灶不明癌（CUP）具有复杂的诊断挑战，其特征是转移性肿瘤来源组织不明，预后较差。本文深入探讨了人工智能（AI）和机器学习（ML）在改变 CUP 诊断、分类和治疗格局方面的新兴意义。经过大量分子谱数据训练的 ML 方法在准确预测组织来源方面显示出了前景。基因组谱分析包括驱动突变和拷贝数变异，通过提供肿瘤类型特异性致癌改变的见解，在 CUP 诊断中发挥着关键作用。突变特征（MS）反映了体细胞突变模式，为 CUP 诊断提供了进一步的见解。已知具有明确病因的 MS，如紫外线（UV）光诱导的 DNA 损伤和烟草暴露，已经在去分化/转分化黑色素瘤和癌病例中被识别。整合基因表达数据和 DNA 甲基化模式的深度学习模型为组织谱系和肿瘤分类提供了见解。在数字病理学中，机器学习算法分析全切片图像以辅助 CUP 分类。最后，基于分子谱的精准肿瘤学提供了独立于原发组织识别的靶向治疗。临床试验将 CUP 患者分配到基于分子的治疗中，包括可靶向的改变和肿瘤突变负担作为免疫治疗生物标志物，已经导致一部分患者的总生存期得到改善。总之，AI 和 ML 驱动的方法通过提高诊断准确性正在改变 CUP 的管理。利用增强的分子谱进行精准肿瘤学有助于识别靶向治疗方法，超越了识别组织来源的需求，最终改善患者的预后。
