Rayfield Corbin A, Grady Fillan, De Leon Gustavo, Rockne Russell, Carrasco Eduardo, Jackson Pamela, Vora Mayur, Johnston Sandra K, Hawkins-Daarud Andrea, Clark-Swanson Kamala R, Whitmire Scott, Gamez Mauricio E, Porter Alyx, Hu Leland, Gonzalez-Cuyar Luis, Bendok Bernard, Vora Sujay, Swanson Kristin R
Corbin A. Rayfield, Fillan Grady, Gustavo De Leon, Eduardo Carrasco, Pamela Jackson, Mayur Vora, Sandra K. Johnston, Andrea Hawkins-Daarud, Kamala R. Clark-Swanson, Scott Whitmire, Mauricio E. Gamez, Alyx Porter, Leland Hu, Bernard Bendok, Sujay Vora, and Kristin R. Swanson, Mayo Clinic, Phoenix, AZ; Russell Rockne, Beckman Research Institute, City of Hope, Duarte, CA; and Sandra K. Johnston and Luis Gonzalez-Cuyar, University of Washington, Seattle, WA.
JCO Clin Cancer Inform. 2018 Dec;2:1-14. doi: 10.1200/CCI.17.00080.
Despite the intra- and intertumoral heterogeneity seen in glioblastoma multiforme (GBM), there is little definitive data on the underlying cause of the differences in patient survivals. Serial imaging assessment of tumor growth allows quantification of tumor growth kinetics (TGK) measured in terms of changes in the velocity of radial expansion seen on imaging. Because a systematic study of this entire TGK phenotype-growth before treatment and during each treatment to recurrence -has never been coordinately studied in GBMs, we sought to identify whether patients cluster into discrete groups on the basis of their TGK.
From our multi-institutional database, we identified 48 patients who underwent maximally safe resection followed by radiotherapy with imaging follow-up through the time of recurrence. The patients were then clustered into two groups through a k-means algorithm taking as input only the TGK before and during treatment.
There was a significant survival difference between the clusters ( P = .003). Paradoxically, patients among the long-lived cluster had significantly larger tumors at diagnosis ( P = .027) and faster growth before treatment ( P = .003) but demonstrated a better response to adjuvant chemotherapy ( P = .048). A predictive model was built to identify which cluster patients would likely fall into on the basis of information that would be available to clinicians immediately after radiotherapy (accuracy, 90.3%).
Dichotomizing the heterogeneity of GBMs into two populations-one faster growing yet more responsive with increased survival and one slower growing yet less responsive with shorter survival-suggests that many patients who receive standard-of-care treatments may get better benefit from select alternative treatments.
尽管多形性胶质母细胞瘤（GBM）存在肿瘤内和肿瘤间的异质性，但关于患者生存差异的根本原因，确切数据很少。对肿瘤生长进行连续成像评估可量化肿瘤生长动力学（TGK），该指标通过成像观察到的径向扩展速度变化来衡量。由于此前从未对GBM患者从治疗前到每次治疗直至复发期间的整个TGK表型生长进行过系统研究，我们试图确定患者是否可根据其TGK聚类为不同组。
从我们的多机构数据库中，我们确定了48例患者，这些患者接受了最大安全切除，随后进行放疗，并在复发前进行了成像随访。然后通过k均值算法将患者聚类为两组，该算法仅将治疗前和治疗期间的TGK作为输入。
两组之间的生存差异显著（P = 0.003）。矛盾的是，长寿组患者在诊断时肿瘤明显更大（P = 0.027），治疗前生长更快（P = 0.003），但对辅助化疗的反应更好（P = 0.048）。建立了一个预测模型，根据放疗后临床医生可立即获得的信息来识别患者可能属于哪一组（准确率为90.3%）。
将GBM的异质性分为两类人群——一类生长较快但对治疗反应更好且生存期延长，另一类生长较慢但对治疗反应较差且生存期较短——这表明许多接受标准治疗的患者可能从某些替代治疗中获益更多。
