Background In recent years, studies have indicated that in response to almost any type of anti-cancer therapy, the patient (the host) may generate pro-tumorigenic and pro-metastatic effects. This phenomenon, called host-response, counteracts the anti-tumor activity of the treatment. We have previously shown that machine learning-based plasma proteomic analysis of the host response may serve as a predictive tool for response in non-small cell lung cancer. Here, we present initial results of a host response-based machine learning classifier that predicts clinical outcome in melanoma patients treated with immune checkpoint inhibitors (ICIs). Methods Plasma samples from melanoma patients (training set; n=32) treated with anti-PD-1 or anti-PD-1 and anti-CTLA-4 combination were obtained at baseline and early on treatment. Response was based on RECIST criteria. Proteomic profiling of the plasma samples was performed using ELISA-based antibody arrays. Machine learning algorithms were used to identify a predictive signature that stratifies between responders and non-responders. The signature was validated on an independent cohort of melanoma patients (validation set; n=14). In addition, advanced bioinformatic analysis was performed in order to identify biological pathways unique to responders and non-responders. Results A 3-protein signature was identified as a predictor of clinical outcome following immunotherapy with an area under the curve (AUC) of the receiver operating characteristics (ROC) plot of 0.88 (p-value 5.84E-05; confidence interval 0.76 – 1.0), and sensitivity and specificity of 0.65 and 0.95, respectively. This signature was successfully validated with AUC of 0.85 (p-value 0.03; confidence interval 0.63 – 1.0), and sensitivity and specificity of 0.75 and 0.9, respectively. To further explore the biological basis of resistance to immunotherapy, we performed a pathway enrichment analysis. Multiple mechanisms for resistance were identified in the non-responder group, including immunosuppression and inflammation associated pathways. Comparison between the two treatment modalities revealed pathways unique to each treatment that involve extracellular modulation, immunosuppression and processes associated with tumor progression, which may imply important differences between the two regimens. Conclusions Our results demonstrate that analyzing the host response to ICI therapy using plasma-based proteomic profiling combined with machine learning algorithms serves as a successful approach for predictive biomarker discovery in melanoma. This bioinformatics-based functional analysis provides insights into mechanisms of resistance and may be used to identify potential strategies for improving clinical outcomes. Ethics Approval The study was approved by the Yale University Institutional Review Ethics Board, approval number 0609001869.
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机译：背景技术近年来，研究表明，响应几乎任何类型的抗癌治疗，患者（宿主）可能会产生促致瘤和促转移效果。这种称为宿主反应的现象抵消了治疗的抗肿瘤活性。我们之前已经表明，基于机器学习的血浆蛋白质组学分析的宿主响应可以作为非小细胞肺癌反应的预测工具。在这里，我们呈现了基于宿主响应的机器学习分类的初始结果，该分类剂预测了用免疫检查点抑制剂（ICIS）治疗的黑色素瘤患者的临床结果。方法在基线和早期处理用抗PD-1或抗PD-1和抗CTLA-4组合处理来自黑素瘤患者（训练套装; N = 32）的血浆样品。响应基于重新标准。使用ELISA基抗体阵列进行等离子体样品的蛋白质组学分析。机器学习算法用于识别响应者和非响应者之间分层的预测签名。签名在独立的黑素瘤患者队列（验证组; N = 14）上验证。此外，进行高级生物信息分析，以识别响应者和非响应者独有的生物途径。结果将3蛋白签名鉴定为在接收器操作特性（ROC）图下的曲线（AUC）下的区域下的临床结果的预测因子（ROC）图0.88（p值5.84e-05;置信区间0.76 - 1.0 ），敏感性和特异性分别为0.65和0.95。此签名已成功验证为0.85（p值0.03;置信区间0.63 - 1.0），敏感性和特异性分别为0.75和0.9。为了进一步探讨抗免疫疗法的生物学依据，我们进行了途径富集分析。在非响应者组中鉴定了多种抗性机制，包括免疫抑制和炎症相关途径。两种治疗方式之间的比较揭示了每种治疗的途径，涉及与肿瘤进展相关的细胞外调节，免疫抑制和方法，这可能意味着两种方案之间的重要差异。结论我们的研究结果表明，使用基于等离子体的蛋白质组学分析与机器学习算法相结合分析对ICI治疗的宿主响应，作为Melanoma中预测生物标志物发现的成功方法。基于生物信息学的功能分析提供了抗抵抗机制的见解，可用于识别改善临床结果的潜在策略。道德批准该研究得到了耶鲁大学机构审查道德委员会的批准，批准号0609001869。
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