Diagnostic assessment of osteosarcoma chemoresistance based on Virtual Clinical Trials.
Diagnostic assessment of osteosarcoma chemoresistance based on Virtual Clinical Trials.
复制标题
基于虚拟临床试验的骨肉瘤化学耐药性的诊断评估。
DOI：
10.1016/j.mehy.2015.06.015
复制
发表时间：
2015-09
影响因子：
4.7
通讯作者：
Bui MM
中科院分区：
文献类型：
--
作者：
Rejniak KA;Lloyd MC;Reed DR;Bui MM
骨肉瘤是儿童和年轻成人患者中最常见的原发性骨肿瘤。骨肉瘤的成功治疗需要手术切除和全身化疗的结合，包括新辅助（手术前）和辅助（手术后）。新辅助化疗后的坏死程度与随后的无病生存率相关。治疗后活细胞少于10%的肿瘤代表预后更好的患者。然而，能够早期预测，例如在治疗前肿瘤活检时，患者对标准化疗的反应将为更个性化的患者护理提供机会。预测结果不佳的患者可以在方案中进行研究，而不是标准设置，以提高治疗成功率。在用化疗剂治疗的骨肉瘤中坏死细胞的出现是肿瘤对药物敏感性的量度。我们假设剩余的活细胞，即，对治疗没有反应的细胞是化学抗性的，并且骨肉瘤治疗前活组织检查中这些化学抗性肿瘤细胞的病理学特征可以预测肿瘤对标准护理化学治疗的反应。该假设可以通过比较治疗前以及治疗后的患者组织病理学样品来检验，以鉴定作为化学抗性细胞特征的形态学和免疫化学细胞特征，即，治疗后存活的细胞。因此，使用在模拟的护理标准化学治疗下肿瘤病理学的动态变化的计算模拟，可以将化学抗性细胞的治疗前和治疗后形态和空间模式结合起来，并将它们与患者临床诊断相关联。我们命名为“虚拟临床试验”的这一程序可以作为一种潜在的预测生物标志物，为肿瘤学家提供一种新的增值决策支持工具。
Osteosarcoma is the most common primary bone tumor in pediatric and young adult patients. Successful treatment of osteosarcomas requires a combination of surgical resection and systemic chemotherapy, both neoadjuvant (prior to surgery) and adjuvant (after surgery). The degree of necrosis following neoadjuvant chemotherapy correlates with the subsequent probability of disease-free survival. Tumors with less than 10% of viable cells after treatment represent patients with a more favorable prognosis. However, being able to predict early, such as at the time of the pre-treatment tumor biopsy, how the patient will respond to the standard chemotherapy would provide an opportunity for more personalized patient care. Patients with unfavorable predictions could be studied in a protocol, rather than a standard setting, towards improving therapeutic success. The onset of necrotic cells in osteosarcomas treated with chemotherapeutic agents is a measure of tumor sensitivity to the drugs. We hypothesize that the remaining viable cells, i.e., cells that have not responded to the treatment, are chemoresistant, and that the pathological characteristics of these chemoresistant tumor cells within the osteosarcoma pre-treatment biopsy can predict tumor response to the standard-of-care chemotherapeutic treatment. This hypothesis can be tested by comparing patient histopathology samples before, as well as after treatment to identify both morphological and immunochemical cellular features that are characteristic of chemoresistant cells, i.e., cells that survived treatment. Consequently, using computational simulations of dynamic changes in tumor pathology under the simulated standard of care chemotherapeutic treatment, one can couple the pre- and post-treatment morphological and spatial patterns of chemoresistant cells, and correlate them with patient clinical diagnoses. This procedure, that we named ‘Virtual Clinical Trials’, can serve as a potential predictive biomarker providing a novel value-added decision support tool for oncologists.