Rajkumar Utkrisht, Prasad Gino, Curtis Ellis J, Wong Ivy Tsz-Lo, Yan Xiaowei, Zhang Shu, Brückner Lotte, Turner Kristen, Wiese Julie, Wahl Justin, Wu Sihan, Theissen Jessica, Fischer Matthias, Chang Howard Y, Henssen Anton G, Mischel Paul S, Bafna Vineet
Department of Computer Science and Engineering, University of California San Diego, San Diego, CA, USA.
Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA.
bioRxiv. 2025 Jun 27:2025.06.23.661188. doi: 10.1101/2025.06.23.661188.
Oncogene amplification is a key driver of cancer pathogenesis and is often mediated by extrachromosomal DNA (ecDNA). EcDNA amplifications are associated with increased pathogenicity of cancer and poorer outcomes for patients. EcDNA can be detected accurately using fluorescence in situ hybridization (FISH) when cells are arrested in metaphase. However, the majority of cancer cells are non-mitotic and must be analyzed in interphase, where it is difficult to discern extrachromosomal amplifications from chromosomal amplifications. Thus, there is a need for methods that accurately predict oncogene amplification status from interphase cells. Here, we present interSeg, a deep learning-based tool to cytogenetically determine the amplification status as EC-amp, HSR-amp, or not amplified from interphase FISH images. We trained and validated interSeg on 652 images (40,446 nuclei). Tests on 215 cultured cell and tissue model images (9,733 nuclei) showed 89% and 97% accuracy at the nuclear and sample levels, respectively. The neuroblastoma patient tissue hold-out set (67 samples and 1,937 nuclei) also revealed 97% accuracy at the sample level in detecting the presence of focal amplification. In experimentally and computationally mixed images, interSeg accurately predicted the level of heterogeneity. The results showcase interSeg as an important method for analyzing oncogene amplifications.
癌基因扩增是癌症发病机制的关键驱动因素，通常由染色体外DNA（ecDNA）介导。ecDNA扩增与癌症致病性增加和患者预后较差有关。当细胞处于中期时，使用荧光原位杂交（FISH）可以准确检测ecDNA。然而，大多数癌细胞是非有丝分裂的，必须在间期进行分析，而在间期很难区分染色体外扩增和染色体扩增。因此，需要能够从间期细胞准确预测癌基因扩增状态的方法。在这里，我们展示了interSeg，这是一种基于深度学习的工具，用于从间期FISH图像中细胞遗传学确定扩增状态为ecDNA扩增（EC-amp）、均质染色区扩增（HSR-amp）或未扩增。我们在652张图像（40446个细胞核）上对interSeg进行了训练和验证。对215张培养细胞和组织模型图像（9733个细胞核）的测试表明，在细胞核和样本水平的准确率分别为89%和97%。神经母细胞瘤患者组织保留集（67个样本和1937个细胞核）在检测局灶性扩增存在时，在样本水平的准确率也达到了97%。在实验和计算混合图像中，interSeg准确预测了异质性水平。结果表明interSeg是分析癌基因扩增的一种重要方法。
