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.

摘要