Protein biomarkers to predict pain outcomes after total pancreatectomy with islet autotransplant

摘要 慢性胰腺炎（CP）通常是一种疼痛和致残性疾病，治疗选择很少。者 疼痛和生活质量（QoL）受损，药物和内窥镜治疗失败，完全切除 胰腺可以考虑进行称为全胰腺切除术和胰岛自体移植（TPIAT）的手术。 对于许多患者来说，TPIAT可以改变生活，减轻疼痛。然而，高达20%的人 TPIAT后明显持续疼痛。我们缺乏客观的措施来预测谁会对TPIAT做出反应。在 对于那些有反应的人，据推测，疼痛的解决仅仅是因为胰腺切除术已经去除了 疼痛的内脏来源，而无反应者有更复杂的疼痛综合征，涉及的变化， 中枢神经系统我们小组在CP（无TPIAT）中的补充工作表明，血浆或 尿生物标志物可以区分疼痛表型。 目前的提案将利用最大的TPIAT数据库和来自多中心研究的生物库， 接受TPIAT（POST研究）的患者，以开发预测TPIAT应答的生物标志物。注：M博士。 POST研究的PI贝林也是本申请的PI。在NIDDK资助的POST研究中， 入组的受试者接受了TPIAT，并在TPIAT之前和之后1年对疼痛和QoL进行了详细的表型分析。 此外，在TPIAT之前，在所有同意的参与者中收集生物标本，包括血浆和尿液 （n=384）和TPIAT后1年（n=183）的一个亚组中进行了面对面随访。根据我们的初步数据， 和其他实验室，我们假设一组来自血液和/或尿液的客观生物标志物可以区分 那些从TPIAT（疼痛减轻/缓解）中受益的人，以及那些反应不佳的人。 这个临床问题特别重要，因为TPIAT是一种主要的、昂贵的干预措施， 终身健康影响。在SA 1中，我们将鉴定TPIAT前收集的血浆生物标志物， 使用POST的样本和数据进行TPIAT后持续疼痛。样本将分为符合FDA要求的 发现和验证组。TPIAT的疼痛反应将通过阿片类药物使用和1年时的疼痛评分来定义。 还将评估QoL和疼痛干扰日常功能的次要指标。在SA 2中，我们将 使用相同的方法来确定预测TPIAT后持续性疼痛的TPIAT前尿液生物标志物。在SA 3中， 我们将使用经过验证的血浆和尿液生物标志物评估从TPIAT前到1年生物标志物的变化 SA 1/2（SA 3a），还使用发现和验证方法来鉴定其他血浆和尿液 生物标志物，可能随着时间的推移在有持续性疼痛的患者与无持续性疼痛的患者中具有不同的模式（SA 3b）。 确定血浆和尿液生物标志物将改善TPIAT患者的选择，减少对TPIAT患者的负面影响。 患者和医疗基础设施。这项研究的数据将确定生物标志物，也可以调查 在更大的CP和其他疼痛综合征人群中。

ABSTRACT Chronic pancreatitis (CP) is an often painful and disabling condition with few treatment options. For those with pain and impaired quality-of-life (QoL) who have failed medical and endoscopic therapies, complete removal of the pancreas may be considered with a procedure called total pancreatectomy with islet autotransplant (TPIAT). For many patients, TPIAT can be life-changing, with meaningful pain reduction. However, up to 20% have significant persistent pain after TPIAT. We lack objective measures to predict who will respond to TPIAT. In those who do respond, it is presumed that the pain is resolved simply because the pancreatectomy has removed the visceral source of pain, while non-responders have more complex pain syndromes involving changes in the central nervous system. Complementary work by our group in CP (without TPIAT) suggests that plasma or urinary biomarkers may distinguish pain phenotypes. The current proposal will leverage the largest TPIAT database and biorepository from a multicenter study of patients undergoing TPIAT (POST study) to develop biomarkers that predict response to TPIAT. Of note: Dr. M. Bellin, the PI of the POST study, is also PI on this application. In the NIDDK-funded POST study, over 400 enrolled participants underwent TPIAT, with detailed phenotyping for pain and QoL before and 1 year after TPIAT. In addition, biospecimens including plasma and urine were collected before TPIAT in all consenting participants (n=384) and 1 year after TPIAT in a subset (n=183) with in-person follow up. Based on preliminary data from our and other labs, we hypothesize that a set of objective biomarkers from blood and/or urine can distinguish those who benefit from TPIAT (pain reduction/relief) from those who respond poorly. This clinical question is particularly important because TPIAT is a major, costly intervention with irreversible lifelong health implications. In SA 1, we will identify plasma biomarkers collected before TPIAT that predict persistent pain after TPIAT using samples and data from POST. Samples will be divided into FDA-compliant discovery and validation groups. Pain response to TPIAT will be defined by opioid use and pain scores at 1 year. Secondary measures for QoL and pain interference with daily function will also be assessed. In SA 2, we will use the same approach to identify pre-TPIAT urine biomarkers that predict persistent pain after TPIAT. In SA 3, we will assess change in biomarkers from pre-TPIAT to 1 year using plasma and urine biomarkers validated in SA 1/2 (SA 3a) and also using a discovery and validation approach to identify additional plasma and urine biomarkers that may have distinct patterns over time in patients with vs. without persistent pain (SA 3b). Identifying plasma and urinary biomarkers will improve TPIAT patient selection, reducing negative impact on patients and health care infrastructure. Data from this study will identify biomarkers that can also be investigated in the larger populations with CP and other pain syndromes.