Predicting the Likelihood of Immune-related Adverse Events in Breast Cancer Patients

摘要 免疫肿瘤学药物明显提高了三阴性乳腺癌（TNBC）的缓解率 患者然而，这些改善是有代价的-- 10-25%的患者将经历免疫相关的疾病。 不良事件（irAE）。这些AE似乎与缓解无关，并且表现为特异质。 例如，肾上腺功能不全可能在患者症状严重并有皮质醇时出现较晚 如果治疗不当可能导致死亡。识别个体患者或患者子集的能力 这些毒性的风险增加或高的患者将以多种方式改善结果并减少伤害。 那些风险最高的人可能会避免某些免疫疗法的治疗，而那些风险增加的人可能会 标记为进行更密切的监测或进行预防性干预，以避免或降低AE的级别。使用 以这种方式收集人口统计学、生物学和遗传学信息，符合精确肿瘤学的努力。 我们正在解决的以癌症为中心的问题是，我们是否可以预测个体 使用年龄、合并症， 电子健康记录（EHR）数据、生活质量（QOL）、不良事件（AE）和遗传变异。我们 假设通过预测分析可以产生对患者将经历irAE的早期洞察 嵌入在决策支持框架内。本提案的总体目标是：（1）尽早破译 哪些患者会出现甲状腺疾病、肺炎、瘙痒症、结肠炎、肝毒性或肾上腺皮质激素依赖 失败并最终影响生活质量;（2）更好地了解患者的遗传特征， 发生irAE的风险。 我们将使用来自早期乳腺癌I-SPY 2试验的丰富多维数据。由于其适应性 平台设计，I-SPY 2提供了在同一研究中研究多种免疫疗法的机会， 使用跨多个站点的标准方法。我们建议：1）制定和评估一个全面的 方法和由此产生的决策支持算法，专为临床医生，研究人员谁帮助管理 护理接受免疫治疗的患者，2）确定新的和注释的单核苷酸 与irAE相关的多态性（SNP），以及3）在两个新的 实验武器我们的计算模型将根据500例I-SPY 2乳腺癌的信息进行训练 接受免疫治疗的试验患者。这项工作的成功完成将增加我们对 irAE的临床、患者报告和遗传因素，并能够早期预测风险人群 在治疗开始之前。

ABSTRACT Immuno-oncology agents have clearly improved rates of response in triple negative breast cancer (TNBC) patients. However, these improvements come at a cost -- 10-25% of patients will experience an immune-related adverse event (irAEs). These AEs do not appear to be associated with response and appear idiosyncratic. Adrenal insufficiency, for example, can appear late when patients are extremely symptomatic and have a cortisol near zero that can lead fatality if improperly treated. The ability to identify individual patients or subsets of patients who are at increased or high risk of these toxicities will improve outcomes and reduce harm in several ways. Those at highest risk may avoid treatment with certain immunotherapies, while those at increased risk could be flagged for closer monitoring or placed upon prophylactic interventions to avoid or downgrade the AE. The use of demographic, biologic and genetic information in this way is in keeping with precision oncology efforts. The cancer-focused question we are addressing is whether we can predict the likelihood of individuals experiencing serious immune-related adverse events following cancer immunotherapy using age, comorbidities, electronic health record (EHR) data, quality of life (QOL), adverse events (AE), and genetic variations. We hypothesize that early insight into which patients will experience irAEs can be generated by predictive analytics embedded within a decision-support framework. The overall goals of this proposal are to: (1) decipher early which patients are going to experience thyroid disease, pneumonitis, pruritus, colitis, hepatoxicity, or adrenal failure and ultimately affect quality of life; and (2) better understand the genetic profile that underlies patients' risk of developing irAEs. We will use a rich multidimensional data from the I-SPY2 trial in early breast cancer. Due to its adaptive platform design, I-SPY2 provides the opportunity to study multiple immunotherapies within in the same study, using standard methodologies across multiple sites. We propose to: 1) develop and evaluate a holistic approach and resulting decision support algorithm, designed for clinician-researchers who help manage the care of patients undergoing immunotherapy, 2) determine both novel and annotated single nucleotide polymorphisms (SNPs) associated with irAEs, and 3) validate the decision support algorithm in two new experimental arms. Our computational models will be trained on information from 500 I-SPY2 breast cancer trial patients undergoing immunotherapy. Successful completion of this work will increase our understanding of the clinical, patient-reported, and genetic factors underlying irAEs and enable early prediction of who is at risk before therapy is initiated.