Glioblastoma is a highly aggressive cancer associated with a dismal prognosis, with a mere 5% of patients surviving beyond five years post diagnosis. Current therapeutic modalities encompass surgical intervention, radiotherapy, chemotherapy, and immune checkpoint inhibitors (ICBs). However, the efficacy of ICBs remains limited in glioblastoma patients, necessitating a proactive approach to anticipate treatment response and resistance. In this comprehensive study, we conducted a rigorous analysis involving two distinct glioblastoma patient cohorts subjected to PD-1 blockade treatments. Our investigation revealed that a significant portion (60%) of patients exhibit persistent disease progression despite ICB intervention. To elucidate the underpinnings of resistance, we characterized the immune profiles of glioblastoma patients with continued cancer progression following anti-PD1 therapy. These profiles revealed multifaceted defects, encompassing compromised macrophage, monocyte, and T follicular helper responses, impaired antigen presentation, aberrant regulatory T cell (Tregs) responses, and heightened expression of immunosuppressive molecules (TGFB, IL2RA, and CD276). Building upon these resistance profiles, we leveraged cutting-edge machine learning algorithms to develop predictive models and accompanying software. This innovative computational tool achieved remarkable success, accurately forecasting the progression status of 82.82% of the glioblastoma patients in our study following ICBs, based on their unique immune characteristics. In conclusion, our pioneering approach advocates for the personalization of immunotherapy in glioblastoma patients. By harnessing patient-specific attributes and computational predictions, we offer a promising avenue for the enhancement of clinical outcomes in the realm of immunotherapy. This paradigm shift towards tailored therapies underscores the potential to revolutionize the management of glioblastoma, opening new horizons for improved patient care.
胶质母细胞瘤是一种高度侵袭性的癌症，预后极差，仅有5%的患者在确诊后能存活超过五年。目前的治疗方式包括手术干预、放疗、化疗以及免疫检查点抑制剂（ICBs）。然而，ICBs在胶质母细胞瘤患者中的疗效仍然有限，因此需要采取主动策略来预测治疗反应和耐药性。在这项全面的研究中，我们对两个接受PD-1阻断治疗的胶质母细胞瘤患者队列进行了严格分析。研究发现，尽管接受了ICB干预，仍有相当一部分患者（60%）表现出持续的疾病进展。为了阐明耐药性的内在机制，我们对接受抗PD1治疗后癌症仍持续进展的胶质母细胞瘤患者的免疫特征进行了分析。这些特征揭示了多方面的缺陷，包括巨噬细胞、单核细胞和滤泡辅助性T细胞反应受损，抗原呈递功能减弱，调节性T细胞（Tregs）反应异常，以及免疫抑制分子（TGFB、IL2RA和CD276）表达增强。基于这些耐药特征，我们利用前沿的机器学习算法开发了预测模型及相关软件。这一创新的计算工具取得了显著成功，根据患者独特的免疫特征，准确预测了研究中82.82%的胶质母细胞瘤患者在接受ICBs后的疾病进展状态。总之，我们的开创性方法提倡对胶质母细胞瘤患者进行个体化免疫治疗。通过利用患者特异性特征和计算预测，我们为改善免疫治疗领域的临床结果提供了一条有前景的途径。这种向个体化治疗的范式转变，突显了革新胶质母细胞瘤治疗管理的潜力，为改善患者护理开辟了新的前景。
Predicting Immunotherapy Outcomes in Glioblastoma Patients through Machine Learning
肿瘤(癌症)患者之家