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

预测乳腺癌患者发生免疫相关不良事件的可能性

• 批准号: 10304516
• 负责人: Amrita Basu Somani
• 金额: $ 40.73万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-15 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10304516
• 关键词: Address; Adrenal Glands; Adrenal gland hypofunction; Adverse event; Affect; Age; Algorithm Design; Algorithms; Autoimmune Diseases; Biological; Breast; Breast Cancer Patient; Caring; Clinical; Clinical Data; Colitis; Computer Models; Data; Data Reporting; Decision Making; Electronic Health Record; Event; Failure; Genetic; Genetic Variation; Genomics; Goals; Hepatotoxicity; Hydrocortisone; Immune; Immunooncology; Immunotherapeutic agent; Immunotherapy; Individual; Interruption; Intervention; Lead; Learning; Life; Machine Learning; Malignant Neoplasms; Measures; Methodology; Methods; Monitor; Neoadjuvant Therapy; Participant; Patient Care Management; Patient Outcomes Assessments; Patient risk; Patients; Performance; Pharmaceutical Preparations; Predictive Analytics; Prophylactic treatment; Pruritus; Pulmonary Inflammation; Quality of life; Reporting; Research Personnel; Risk; Running; Severities; Single Nucleotide Polymorphism; Site; Symptoms; Testing; Thyroid Diseases; Toxic effect; Training; Withholding Treatment; Work; base; cancer clinical trial; cancer immunotherapy; comorbidity; computerized tools; cost; data tools; demographics; design; experience; experimental arm; genetic analysis; genetic information; health data; health related quality of life; high risk; holistic approach; immune-related adverse events; improved; improved outcome; individual patient; individualized medicine; insight; malignant breast neoplasm; multidimensional data; novel; patient subsets; precision oncology; predictive modeling; prevent; prophylactic; prospective; response; support tools; treatment arm; triple-negative invasive breast carcinoma

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.

抽象的 免疫肿瘤药显然改善了三重阴性乳腺癌的反应率（TNBC） 患者。但是，这些改进是有代价的 - 10-25％的患者将经历与免疫有关的 不利事件（伊拉斯）。这些AE似乎与响应无关，并且看起来特质。 例如，肾上腺功能不全时，患者极有症状并且有皮质醇时会出现较晚 如果不正确治疗，可能会导致死亡的零接近。识别患者或子集的能力 这些毒性的风险增加或高风险将改善预后并以多种方式减少伤害。 那些处于最高风险的人可能会避免通过某些免疫疗法治疗，而风险增加的人可能是 标记以进行更紧密的监测或进行预防干预措施，以避免或降级AE。使用 人口统计学，生物学和遗传信息以这种方式与精确肿瘤学的努力保持一致。 我们要解决的以癌症为中心的问题是我们是否可以预测个人的可能性 使用年龄，合并症，癌症免疫疗法后，经历严重的免疫相关事件， 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 假设可以通过预测分析能够产生早期的洞察力。 嵌入在决策支持框架中。该提议的总体目标是：（1）提早解密 哪些患者将患甲状腺疾病，肺炎，瘙痒，结肠炎，肝毒性或肾上腺 失败并最终影响生活质量； （2）更好地了解基于患者的遗传特征 发展伊拉斯的风险。 我们将在早期乳腺癌中使用I-SPY2试验的丰富多维数据。由于其自适应 平台设计，I-SPY2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）开发和评估整体 方法和由此产生的决策支持算法，专为临床医生研究人员而设计，他们帮助管理 护理接受免疫疗法的患者，2）确定新颖和注释的单核苷酸 与IRAE相关的多态性（SNP）和3）验证两个新的决策支持算法 实验臂。我们的计算模型将接受来自500 I-SPY2乳腺癌的信息的培训 试验患者接受免疫疗法。成功完成这项工作将增加我们对 伊拉斯的临床，患者报告和遗传因素，并能够早期预测谁处于危险之中 在开始治疗之前。