Graduate Institute of Biomedical Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan; Graduate Institute of Applied Science and Technology, National Taiwan University of Science and Technology, Taipei, Taiwan.
Graduate Institute of Applied Science and Technology, National Taiwan University of Science and Technology, Taipei, Taiwan.
Comput Med Imaging Graph. 2023 Jul;107:102233. doi: 10.1016/j.compmedimag.2023.102233. Epub 2023 Apr 12.
Inhibition of pathological angiogenesis has become one of the first FDA approved targeted therapies widely tested in anti-cancer treatment, i.e. VEGF-targeting monoclonal antibody bevacizumab, in combination with chemotherapy for frontline and maintenance therapy for women with newly diagnosed ovarian cancer. Identification of the best predictive biomarkers of bevacizumab response is necessary in order to select patients most likely to benefit from this therapy. Hence, this study investigates the protein expression patterns on immunohistochemical whole slide images of three angiogenesis related proteins, including Vascular endothelial growth factor, Angiopoietin 2 and Pyruvate kinase isoform M2, and develops an interpretable and annotation-free attention based deep learning ensemble framework to predict the bevacizumab therapeutic effect on patients with epithelial ovarian cancer or peritoneal serous papillary carcinoma using tissue microarrays (TMAs). In evaluation with five-fold cross validation, the proposed ensemble model using the protein expressions of both Pyruvate kinase isoform M2 and Angiopoietin 2 achieves a notably high F-score (0.99±0.02), accuracy (0.99±0.03), precision (0.99±0.02), recall (0.99±0.02) and AUC (1.00±0). Kaplan-Meier progression free survival analysis confirms that the proposed ensemble is able to identify patients in the predictive therapeutic sensitive group with low cancer recurrence (p<0.001), and the Cox proportional hazards model analysis further confirms the above statement (p=0.012). In conclusion, the experimental results demonstrate that the proposed ensemble model using the protein expressions of both Pyruvate kinase isoform M2 and Angiopoietin 2 can assist treatment planning of bevacizumab targeted therapy for patients with ovarian cancer.
病理性血管生成的抑制已成为第一种获得美国食品和药物管理局（FDA）批准的靶向治疗方法之一，广泛应用于癌症治疗中，例如 VEGF 靶向单克隆抗体贝伐珠单抗，与化疗联合用于新诊断的卵巢癌的一线治疗和维持治疗。为了选择最有可能从这种治疗中获益的患者，有必要确定贝伐珠单抗反应的最佳预测生物标志物。因此，本研究通过免疫组化全切片图像，研究了三种与血管生成相关的蛋白质（包括血管内皮生长因子、血管生成素 2 和丙酮酸激酶同工酶 M2）的蛋白表达模式，并开发了一种可解释的、无注释的基于注意力的深度学习集成框架，使用组织微阵列（TMA）预测上皮性卵巢癌或腹膜浆液性乳头状癌患者对贝伐珠单抗的治疗效果。在五重交叉验证评估中，使用丙酮酸激酶同工酶 M2 和血管生成素 2 的蛋白表达的集成模型达到了显著高的 F 分数（0.99±0.02）、准确性（0.99±0.03）、精度（0.99±0.02）、召回率（0.99±0.02）和 AUC（1.00±0）。Kaplan-Meier 无进展生存分析证实，该集成模型能够识别出预测治疗敏感组中癌症复发率较低的患者（p<0.001），Cox 比例风险模型分析进一步证实了这一说法（p=0.012）。总之，实验结果表明，使用丙酮酸激酶同工酶 M2 和血管生成素 2 的蛋白表达的集成模型可以辅助卵巢癌患者贝伐珠单抗靶向治疗的治疗计划。
