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: 10242677
• 负责人: Iris Z Jaffe
• 金额: $ 61.47万
• 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/10242677
• 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; efficacy evaluation; experience; imaging modality; implementation facilitation; 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，生存率提高，与治疗相关的心血管（CV）并发症 已经变得更加普遍。传统化疗药物的效果通常是众所周知的，但 小分子抑制剂和免疫治疗剂的结合导致了新的 意想不到的毒性。其中许多问题背后的机械驱动因素尚未得到很好的描述， 这既有损于适当的监测，也有损于有效的干预。这一点由于依赖于 CV毒性模型不能完全概括人类癌症的复杂情况。在体外 研究允许解剖响应于药物暴露的细胞和分子改变，它们缺乏药物暴露的背景。 导致发病的整个有机体。啮齿动物模型在定义 治疗引起的CV并发症的基本特征，但是， 暴露于治疗剂的持续时间和不存在可能影响结果的合并症。此外，委员会认为， 它们的小尺寸和短寿命限制了仪器、纵向分析和重复取样。宠物狗 自发性癌症患者常规接受已知会产生CV毒性的抗癌药物治疗，包括 阿霉素、酪氨酸激酶抑制剂和最近的免疫检查点抑制剂，因此可以提供 在临床上更相关的背景下进行机械询问的机会，以弥合与细胞的差距， 从老鼠到人类其较大的尺寸和较长的寿命允许在设置中使用前瞻性研究设计 标准的癌症治疗，更接近代表人类的经验，从而克服了一些 啮齿动物模型的局限性。因此，这项建议的基本前提是， 癌症治疗诱导的CV毒性的模型系统足以有效地询问 驱动因素和评估治疗干预的方法。相反，我们需要协调一致，综合努力， 需要多种体外和体内模型系统来有效地识别和验证 早期干预的生物标志物，评估新的治疗方法，以解决并发症，并最终开发 预测联合治疗背景下潜在CV毒性的算法。因此，我们建议， 纳入了用已知诱导癌症的药物治疗的自发性癌症犬的数据。 CV毒性将允许更准确地表征和确认关键机制驱动因素， 治疗性干预策略对于改善人类结果至关重要。为了实现这一目标，我们创造了 一个非还原论的多物种框架，用于分析实验室、小鼠模型、狗 自发性癌症和人类患者。本提案中的研究将证明并优化这一点 使用两种已确定但独特的CV毒性的新型平台，限制了癌症患者的有效治疗 - 蒽环类药物诱导的心脏毒性和VEGF诱导的高血压-最终创造了一个蓝图，以更好地 解决现有和紧急癌症治疗诱导的CV毒性，并提高长期生存率。