Department of Internal Medicine I, Ulm University Hospital, Ulm, Germany.
Institute of Medical Systems Biology, Ulm University, Ulm, Germany.
PLoS One. 2024 Jun 14;19(6):e0304324. doi: 10.1371/journal.pone.0304324. eCollection 2024.
Anti-vascular endothelial growth factor (VEGF) monoclonal antibodies (mAbs) are widely used for tumor treatment, including metastatic colorectal cancer (mCRC). So far, there are no biomarkers that reliably predict resistance to anti-VEGF mAbs like bevacizumab. A biomarker-guided strategy for early and accurate assessment of resistance could avoid the use of non-effective treatment and improve patient outcomes. We hypothesized that repeated analysis of multiple cytokines and angiogenic growth factors (CAFs) before and during treatment using machine learning could provide an accurate and earlier, i.e., 100 days before conventional radiologic staging, prediction of resistance to first-line mCRC treatment with FOLFOX plus bevacizumab.
15 German and Austrian centers prospectively recruited 50 mCRC patients receiving FOLFOX plus bevacizumab as first-line treatment. Plasma samples were collected every two weeks until radiologic progression (RECIST 1.1) as determined by CT scans performed every 2 months. 102 pre-selected CAFs were centrally analyzed using a cytokine multiplex assay (Luminex, Myriad RBM).
Using random forests, we developed a predictive machine learning model that discriminated between the situations of "no progress within 100 days before radiological progress" and "progress within 100 days before radiological progress". We could further identify a combination of ten out of the 102 CAF markers, which fulfilled this task with 78.2% accuracy, 71.8% sensitivity, and 82.5% specificity.
We identified a CAF marker combination that indicates treatment resistance to FOLFOX plus bevacizumab in patients with mCRC within 100 days prior to radiologic progress.
抗血管内皮生长因子（VEGF）单克隆抗体（mAbs）广泛用于肿瘤治疗，包括转移性结直肠癌（mCRC）。到目前为止，还没有可靠的生物标志物来预测抗 VEGF mAbs（如贝伐珠单抗）的耐药性。一种基于生物标志物的策略，可以对耐药性进行早期和准确的评估，从而避免使用非有效治疗方法，并改善患者的预后。我们假设，使用机器学习对治疗前和治疗过程中的多种细胞因子和血管生成生长因子（CAFs）进行重复分析，可以提供一种准确的、更早的方法，即在常规放射分期前 100 天，预测一线 mCRC 治疗（FOLFOX 加贝伐珠单抗）的耐药性。
15 家德国和奥地利中心前瞻性招募了 50 名接受 FOLFOX 加贝伐珠单抗作为一线治疗的 mCRC 患者。每两周采集一次血浆样本，直到根据每 2 个月进行的 CT 扫描确定的放射进展（RECIST 1.1）。使用细胞因子多重分析（Luminex，Myriad RBM）对 102 个预先选择的 CAFs 进行了中心分析。
使用随机森林，我们开发了一种预测性机器学习模型，可区分“在放射学进展前 100 天内无进展”和“在放射学进展前 100 天内进展”两种情况。我们还可以进一步确定 102 个 CAF 标志物中的十种标志物组合，该组合以 78.2%的准确率、71.8%的灵敏度和 82.5%的特异性完成了这项任务。
我们确定了一种 CAF 标志物组合，可以在放射学进展前 100 天内预测 mCRC 患者对 FOLFOX 加贝伐珠单抗的治疗耐药性。
