Credentialing a Cross-Species Platform to Investigate Cancer Therapy-Associated Cardiovascular Toxicity

认证跨物种平台来研究癌症治疗相关的心血管毒性

• 批准号: 10001482
• 负责人: Iris Z Jaffe
• 金额: $ 66.16万
• 依托单位: TUFTS MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10001482
• 关键词: Address; Animal Experimentation; Anthracycline; Antihypertensive Agents; Antineoplastic Agents; Arrhythmia; Automobile Driving; Autophagocytosis; Biological Markers; Biological Models; Biology; Blood Pressure; Blood Vessels; Cancer Patient; Canis familiaris; Cardiac Myocytes; Cardiotoxicity; Cardiovascular Abnormalities; Cardiovascular Models; Cardiovascular system; Cells; Characteristics; Clinical; Combined Modality Therapy; Credentialing; Data; Data Analyses; Development; Dissection; Dose; Doxorubicin; Drug Combinations; Drug Exposure; Early Diagnosis; Early Intervention; Echocardiography; Endothelial Cells; Endothelin; Event; Exhibits; Exposure to; Future; Goals; Health; Heart failure; Human; Hypertension; Image; Immune checkpoint inhibitor; Immunotherapeutic agent; Impairment; In Vitro; Intervention; KDR gene; Laboratory mice; Long Term Survivorship; Longevity; Malignant Neoplasms; Medical; Methodology; Modality; Modeling; Molecular; Monitor; Mus; New Agents; Outcome; Pathogenesis; Pathway interactions; Patients; Pharmaceutical Preparations; Phosphopeptides; Process; Prospective Studies; Protocols documentation; Quality of life; Research Design; Rodent; Rodent Model; Sampling; Systemic hypertension; Therapeutic; Therapeutic Intervention; TimeLine; Toxic effect; Translations; Treatment-Related Cancer; Tyrosine Kinase Inhibitor; Untranslated RNA; Validation; Whole Organism; Work; base; cancer care; cancer therapy; cardioprotection; clinically relevant; comorbidity; data integration; early detection biomarkers; effective intervention; effective therapy; experience; imaging modality; improved; improved outcome; in vivo; in vivo Model; inhibitor/antagonist; innovation; instrumentation; longitudinal analysis; mouse model; novel; novel therapeutics; prediction algorithm; predictive marker; prevent; prospective; research clinical testing; response; small molecule inhibitor; survivorship; treatment strategy

As survival improves with advances in cancer care, cardiovascular (CV) complications associated with treatment have become more prevalent. Effects of traditional chemotherapeutics are generally well known, but incorporation of small molecule inhibitors and immunotherapeutics has led to the emergence of new and unexpected toxicities. The mechanistic drivers underlying many of these have not been well characterized, undermining both appropriate monitoring and effective intervention. This is further complicated by reliance upon models of CV toxicity that do not fully recapitulate the complicated landscape of human cancer. While in vitro studies permit dissection of cellular and molecular alterations in response to drug exposure, they lack context of the whole organism that contributes to pathogenesis. Rodent models have been instrumental in defining fundamental characteristics of treatment induced CV complications, but, there are significant differences in duration of exposure to therapeutics and an absence of co-morbidities that likely influence outcome. Moreover, their small size and short lifespan limit instrumentation, longitudinal analysis, and repeated sampling. Pet dogs with spontaneous cancer are routinely treated with anti-cancer agents known to produce CV toxicity including doxorubicin, tyrosine kinase inhibitors, and more recently immune checkpoint inhibitors and may thus provide an opportunity for mechanistic interrogation in a more clinically relevant context to bridge the gap from cells and mice to humans. Their larger size and longer lifespan permit the use of prospective study designs in the setting of standard cancer treatment that more closely represent the human experience, thereby overcoming some limitations of rodent models. As such, the fundamental premise underlying this proposal is that no single model system of cancer treatment-induced CV toxicity is sufficient to effectively interrogate mechanistic drivers and assess approaches to therapeutic intervention. Instead, a coordinated, integrated effort across the landscape of multiple in vitro and in vivo model systems is required to efficiently identify and validate biomarkers for early intervention, evaluate novel treatments to address complications, and ultimately develop algorithms for predicting potential CV toxicity in the setting of combination therapy. We therefore propose that inclusion of data generated from dogs with spontaneous cancer treated with agents known to induce CV toxicity will permit a more accurate characterization and confirmation of key mechanistic drivers and therapeutic intervention strategies critical for advancing human outcomes. To accomplish this, we created a non-reductionist, multi-species framework for analyzing data generated in the laboratory, mouse models, dogs with spontaneous cancer, and human patients. The studies in this proposal will credential and optimize this novel platform using two established yet unique CV toxicities that constrain effective treatment in cancer patients -anthracycline induced cardiotoxicity and VEGFRI induced hypertension- ultimately creating a blueprint to better address both existing and emergent cancer treatment induced CV toxicities and enhance long-term survivorship.

随着癌症治疗的进步，生存率有所提高，与治疗相关的心血管 (CV) 并发症也随之出现 已变得更加普遍。传统化疗药物的效果众所周知，但 小分子抑制剂和免疫治疗的结合导致了新的和 意想不到的毒性。其中许多潜在的机械驱动因素尚未得到很好的表征， 破坏适当的监测和有效的干预。由于依赖，这变得更加复杂 CV毒性模型不能完全概括人类癌症的复杂情况。而在体外 研究允许剖析响应药物暴露的细胞和分子变化，但它们缺乏相关背景 有助于发病的整个有机体。啮齿动物模型有助于定义 治疗引起的 CV 并发症的基本特征，但是，存在显着差异 接受治疗的持续时间以及不存在可能影响结果的合并症。而且， 它们的尺寸小和寿命短限制了仪器仪表、纵向分析和重复采样。宠物狗 患有自发性癌症的患者通常使用已知会产生心血管毒性的抗癌药物进行治疗，包括 阿霉素、酪氨酸激酶抑制剂以及最近的免疫检查点抑制剂，因此可能提供 在更临床相关的背景下进行机械询问的机会，以弥合细胞和 老鼠对人类。它们更大的尺寸和更长的寿命允许在该环境中使用前瞻性研究设计 标准癌症治疗更接近地代表人类经验，从而克服了一些 啮齿动物模型的局限性。因此，该提案的基本前提是，没有任何单一的 癌症治疗引起的 CV 毒性模型系统足以有效地探究机制 驱动因素并评估治疗干预方法。相反，跨部门协调一致的努力 需要多个体外和体内模型系统的景观才能有效地识别和验证 用于早期干预的生物标志物，评估解决并发症的新疗法，并最终开发 预测联合治疗中潜在的 CV 毒性的算法。因此我们建议 包含来自接受已知诱发癌症的药物治疗的患有自发性癌症的狗产生的数据 CV毒性将允许更准确地表征和确认关键机械驱动因素和 治疗干预策略对于改善人类结局至关重要。为了实现这一目标，我们创建了 用于分析实验室、小鼠模型、狗生成的数据的非还原论、多物种框架 自发性癌症和人类患者。本提案中的研究将验证并优化该方案 新颖的平台使用两种已确定但独特的心血管毒性，限制了癌症患者的有效治疗 -蒽环类药物诱发的心脏毒性和 VEGFRI 诱发的高血压——最终为更好地制定蓝图 解决现有和新出现的癌症治疗引起的心血管毒性并提高长期生存率。