Cardillo Nicholas, Devor Eric J, Pedra Nobre Silvana, Newtson Andreea, Leslie Kimberly, Bender David P, Smith Brian J, Goodheart Michael J, Gonzalez-Bosquet Jesus
Department of Obstetrics and Gynecology, University of Iowa, 200 Hawkins Dr., Iowa City, IA 52242, USA.
Nebraska Medical Center, Division of Gynecologic Oncology, University of Nebraska, Omaha, NE 68198, USA.
Cancers (Basel). 2022 Jul 21;14(14):3554. doi: 10.3390/cancers14143554.
Advanced high-grade serous (HGSC) ovarian cancer is treated with either primary surgery followed by chemotherapy or neoadjuvant chemotherapy followed by interval surgery. The decision to proceed with surgery primarily or after chemotherapy is based on a surgeon's clinical assessment and prediction of an optimal outcome. Optimal and complete cytoreductive surgery are correlated with improved overall survival. This clinical assessment results in an optimal surgery approximately 70% of the time. We hypothesize that this prediction can be improved by using biological tumor data to predict optimal cytoreduction. With access to a large biobank of ovarian cancer tumors, we obtained genomic data on 83 patients encompassing gene expression, exon expression, long non-coding RNA, micro RNA, single nucleotide variants, copy number variation, DNA methylation, and fusion transcripts. We then used statistical learning methods (lasso regression) to integrate these data with pre-operative clinical information to create predictive models to discriminate which patient would have an optimal or complete cytoreductive outcome. These models were then validated within The Cancer Genome Atlas (TCGA) HGSC database and using machine learning methods (TensorFlow). Of the 124 models created and validated for optimal cytoreduction, 21 performed at least equal to, if not better than, our historical clinical rate of optimal debulking in advanced-stage HGSC as a control. Of the 89 models created to predict complete cytoreduction, 37 have the potential to outperform clinical decision-making. Prospective validation of these models could result in improving our ability to objectively predict which patients will undergo optimal cytoreduction and, therefore, improve our ovarian cancer outcomes.
晚期高级别浆液性（HGSC）卵巢癌的治疗方法为：要么先进行手术，然后进行化疗；要么先进行新辅助化疗，然后进行间隔手术。选择主要在化疗前还是化疗后进行手术，是基于外科医生的临床评估以及对最佳治疗结果的预测。最佳的完全细胞减灭术与总体生存率的提高相关。这种临床评估大约70%的情况下能实现最佳手术效果。我们假设，通过使用生物学肿瘤数据来预测最佳细胞减灭术，可以改善这种预测。利用获取的大量卵巢癌肿瘤生物样本库，我们获得了83名患者的基因组数据，包括基因表达、外显子表达、长链非编码RNA、微小RNA、单核苷酸变异、拷贝数变异、DNA甲基化和融合转录本。然后，我们使用统计学习方法（套索回归）将这些数据与术前临床信息相结合，创建预测模型，以区分哪些患者会有最佳或完全细胞减灭术的结果。然后，这些模型在癌症基因组图谱（TCGA）HGSC数据库中并使用机器学习方法（TensorFlow）进行了验证。在为最佳细胞减灭术创建并验证的124个模型中，有21个模型的表现至少与作为对照的晚期HGSC中我们的历史最佳减瘤临床率相当，甚至可能更好。在为预测完全细胞减灭术创建的89个模型中，有37个模型有可能优于临床决策。对这些模型进行前瞻性验证可能会提高我们客观预测哪些患者将接受最佳细胞减灭术的能力，从而改善我们的卵巢癌治疗效果。
