Purpose: Development of a computational biomarker to predict, prior to treatment, the response to CDK4/6 inhibition (CDK4/6i) in combination with endocrine therapy in patients with breast cancer. Experimental design: A mechanistic mathematical model that accounts for protein signaling and drug mechanisms of action was developed and trained on extensive, publicly available data from breast cancer cell lines. The model was built to provide a patient-specific response score based on the expression of six genes (CCND1, CCNE1, ESR1, RB1, MYC and CDKN1A). The model was validated in five independent cohorts of 148 patients in total with early-stage or advanced breast cancer treated with endocrine therapy and CDK4/6i. Response was measured either by evaluating Ki67 levels and PAM50 risk of relapse (ROR) after neoadjuvant treatment or by evaluating progression-free survival (PFS). Results: The model showed significant association with patient´s outcomes in all five cohorts. The model predicted high Ki67 (area under the curve; AUC (95% confidence interval) of 0.80 (0.64 - 0.92), 0.81 (0.60 - 1.00) and 0.80 (0.65 - 0.93)) and high PAM50 ROR (AUC of 0.78 (0.64 - 0.89)). This observation was not obtained in patients treated with chemotherapy. In the other cohorts, patient stratification based on the model prediction was significantly associated with PFS (hazard ratio=2.92 (95% CI 1.08 - 7.86), p=0.034 and HR=2.16 (1.02 4.55), p=0.043). Conclusion: A mathematical modeling approach accurately predicts patient outcome following CDK4/6i plus endocrine therapy, which marks a step towards more personalized treatments in patients with Luminal B breast cancer.

中文翻译：

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计算模型预测接受内分泌治疗和 CDK4/6 抑制治疗的 Luminal B 乳腺癌患者的预后


目的：开发一种计算生物标志物，用于在治疗前预测乳腺癌患者对 CDK4/6 抑制 (CDK4/6i) 与内分泌治疗相结合的反应。实验设计：开发了一种解释蛋白质信号传导和药物作用机制的机械数学模型，并根据来自乳腺癌细胞系的广泛公开数据进行了训练。该模型旨在根据六种基因（CCND1、CCNE1、ESR1、RB1、MYC 和 CDKN1A）的表达提供患者特异性反应评分。该模型在五个独立队列中得到验证，该队列共有 148 名接受内分泌治疗和 CDK4/6i 治疗的早期或晚期乳腺癌患者。通过评估新辅助治疗后的 Ki67 水平和 PAM50 复发风险 (ROR) 或评估无进展生存期 (PFS) 来测量疗效。结果：该模型显示与所有五个队列中患者的结果显着相关。该模型预测高 Ki67（曲线下面积；AUC（95% 置信区间）为 0.80 (0.64 - 0.92)、0.81 (0.60 - 1.00) 和 0.80 (0.65 - 0.93)）和高 PAM50 ROR（AUC 为 0.78 (0.64) - 0.89））。这一观察结果并未在接受化疗的患者中获得。在其他队列中，基于模型预测的患者分层与 PFS 显着相关（风险比=2.92（95% CI 1.08 - 7.86），p=0.034，HR=2.16（1.02 4.55），p=0.043）。结论：数学建模方法可以准确预测 CDK4/6i 加内分泌治疗后的患者结果，这标志着 Luminal B 乳腺癌患者朝着更加个性化的治疗迈出了一步。