Ge Zhicheng, Wang Jing, He Libing, Zhao Meng, Si Yang, Chang Siyuan, Zhang Guoyan, Cheng Shan, Ding Wei
Department of General Surgery, Beijing Friendship Hospital, Capital Medical University, Beijing, 100050, People's Republic of China.
Department of Medical Genetics and Developmental Biology, School of Basic Medical Sciences, Capital Medical University, Beijing, 100069, People's Republic of China.
Discov Oncol. 2024 Oct 15;15(1):564. doi: 10.1007/s12672-024-01442-x.
Biomarkers are not only of significant importance for cancer diagnosis and selection of treatment plans but also recently increasingly used for the evaluation of malignancy development and tumor heterogeneity. Large-size tumors from clinical patients can be unique and valuable sources for the study of cancer progression, particularly to the extent of intratumoral heterogeneity. In the present study, we obtained a series of post-surgery puncture samples from a breast cancer patient with a 4 × 3.5 × 2 cm tumor in its original size. Immunohistochemistry for Ki-67, COX-2, and CA IX was performed and the expression levels within the breast cancer tumor mass were evaluated in the reconstructed 3D models. To further evaluate the intratumoral heterogeneity, we performed high throughput whole transcriptome sequencing of 12 samples from different spatial positions within the tumor tissue. Comparing the reconstructed 3D distribution of biomarkers with projected tumor growth models, asymmetric and heterogeneous expansion of tumor mass was found to be possibly influenced by factors such as blood supply, inflammation and/or hypoxia stimulations, as suggested from the correlation between the results of Ki-67 and CA IX or COX-2 staining. Furthermore, high-throughput RNA sequencing data provided additional information for profiling the intratumoral heterogeneity and expanded the understanding of cancer progression. Digital technology for medical imaging once properly integrated with molecular pathology examinations will become particularly helpful in dissecting out in-depth information for precision medicine. We prospect that this approach, facilitated by rapidly advancing artificial intelligence, could provide new insights for clinical decision-making in the future. Strategies for the continuous development from the present study for better performance and application were discussed.
生物标志物不仅对癌症诊断和治疗方案的选择具有重要意义,而且最近越来越多地用于评估恶性肿瘤的发展和肿瘤异质性。临床患者的大尺寸肿瘤可能是研究癌症进展的独特且有价值的来源,特别是对于肿瘤内异质性的程度而言。在本研究中,我们从一名乳腺癌患者身上获取了一系列术后穿刺样本,该患者原发肿瘤大小为4×3.5×2厘米。对Ki-67、COX-2和CA IX进行了免疫组织化学检测,并在重建的三维模型中评估了乳腺癌肿瘤块内的表达水平。为了进一步评估肿瘤内异质性,我们对肿瘤组织内不同空间位置的12个样本进行了高通量全转录组测序。将生物标志物的重建三维分布与预测的肿瘤生长模型进行比较,发现肿瘤块的不对称和异质性扩展可能受血液供应、炎症和/或缺氧刺激等因素影响,这从Ki-67与CA IX或COX-2染色结果之间的相关性可以看出。此外,高通量RNA测序数据为描绘肿瘤内异质性提供了额外信息,并扩展了对癌症进展的理解。医学成像数字技术一旦与分子病理学检查适当整合,将特别有助于剖析精准医学的深入信息。我们预期,在快速发展的人工智能推动下,这种方法可能为未来的临床决策提供新见解。本文讨论了从本研究持续发展以实现更好性能和应用的策略。