Carroll Molly J, Kaipio Katja, Hynninen Johanna, Carpen Olli, Hautaniemi Sampsa, Page David, Kreeger Pamela K
Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA.
Research Center for Cancer, Infections and Immunity, Institute of Biomedicine, University of Turku, FI-20014 Turku, Finland.
Cancers (Basel). 2022 Sep 1;14(17):4291. doi: 10.3390/cancers14174291.
The time between the last cycle of chemotherapy and recurrence, the platinum-free interval (PFI), predicts overall survival in high-grade serous ovarian cancer (HGSOC). To identify secreted proteins associated with a shorter PFI, we utilized machine learning to predict the PFI from ascites composition. Ascites from stage III/IV HGSOC patients treated with neoadjuvant chemotherapy (NACT) or primary debulking surgery (PDS) were screened for secreted proteins and Lasso regression models were built to predict the PFI. Through regularization techniques, the number of analytes used in each model was reduced; to minimize overfitting, we utilized an analysis of model robustness. This resulted in models with 26 analytes and a root-mean-square error (RMSE) of 19 days for the NACT cohort and 16 analytes and an RMSE of 7 days for the PDS cohort. High concentrations of MMP-2 and EMMPRIN correlated with a shorter PFI in the NACT patients, whereas high concentrations of uPA Urokinase and MMP-3 correlated with a shorter PFI in PDS patients. Our results suggest that the analysis of ascites may be useful for outcome prediction and identified factors in the tumor microenvironment that may lead to worse outcomes. Our approach to tuning for model stability, rather than only model accuracy, may be applicable to other biomarker discovery tasks.
末次化疗周期与复发之间的时间间隔，即无铂间期（PFI），可预测高级别浆液性卵巢癌（HGSOC）的总生存期。为了识别与较短PFI相关的分泌蛋白，我们利用机器学习从腹水成分预测PFI。对接受新辅助化疗（NACT）或初次肿瘤细胞减灭术（PDS）治疗的III/IV期HGSOC患者的腹水进行分泌蛋白筛查，并建立套索回归模型来预测PFI。通过正则化技术，减少了每个模型中使用的分析物数量；为了最小化过度拟合，我们对模型稳健性进行了分析。这产生了两个模型，NACT队列的模型有26种分析物，均方根误差（RMSE）为19天，PDS队列的模型有16种分析物，RMSE为7天。在NACT患者中，高浓度的基质金属蛋白酶-2（MMP-2）和促间质金属蛋白酶诱导因子（EMMPRIN）与较短的PFI相关，而在PDS患者中，高浓度的尿激酶型纤溶酶原激活剂（uPA）和基质金属蛋白酶-3（MMP-3）与较短的PFI相关。我们的结果表明，腹水分析可能有助于预后预测，并识别出肿瘤微环境中可能导致较差预后的因素。我们调整模型稳定性而非仅关注模型准确性的方法，可能适用于其他生物标志物发现任务。
