Background

The clinical course of idiopathic pulmonary fibrosis (IPF) is unpredictable. Clinical prediction tools are not accurate enough to predict disease outcomes.

Methods

We enrolled patients with IPF diagnosis in a six-cohort study at Yale University (New Haven, CT, USA), Imperial College London (London, UK), University of Chicago (Chicago, IL, USA), University of Pittsburgh (Pittsburgh, PA, USA), University of Freiburg (Freiburg im Breisgau, Germany), and Brigham and Women's Hospital–Harvard Medical School (Boston, MA, USA). Peripheral blood mononuclear cells or whole blood were collected at baseline from 425 participants and from 98 patients (23%) during 4–6 years' follow-up. A 52-gene signature was measured by the nCounter analysis system in four cohorts and extracted from microarray data (GeneChip) in the other two. We used the Scoring Algorithm for Molecular Subphenotypes (SAMS) to classify patients into low-risk or high-risk groups based on the 52-gene signature. We studied mortality with a competing risk model and transplant-free survival with a Cox proportional hazards model. We analysed timecourse data and response to antifibrotic drugs with linear mixed effect models.

Findings

The application of SAMS to the 52-gene signature identified two groups of patients with IPF (low-risk and high-risk), with significant differences in mortality or transplant-free survival in each of the six cohorts (hazard ratio [HR] range 2·03–4·37). Pooled data showed similar results for mortality (HR 2·18, 95% CI 1·53–3·09; p<0·0001) or transplant-free survival (2·04, 1·52–2·74; p<0·0001). Adding 52-gene risk profiles to the Gender, Age, and Physiology index significantly improved its mortality predictive accuracy. Temporal changes in SAMS scores were associated with changes in forced vital capacity (FVC) in two cohorts. Untreated patients did not shift their risk profile over time. A simultaneous increase in up score and decrease in down score was predictive of decreased transplant-free survival (3·18, 1·16–8·76; p=0·025) in the Pittsburgh cohort. A simultaneous decrease in up score and increase in down score after initiation of antifibrotic drugs was associated with a significant (p=0·0050) improvement in FVC in the Yale cohort.

Interpretation

The peripheral blood 52-gene expression signature is predictive of outcome in patients with IPF. The potential value of the 52-gene signature in predicting response to therapy should be determined in prospective studies.

Funding

The Pulmonary Fibrosis Foundation, the Harold Amos Medical Faculty Development Program of the Robert Wood Johnson Foundation, and the National Heart, Lung, and Blood Institute of the US National Institutes of Health.

中文翻译：

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验证52基因风险谱用于特发性肺纤维化患者预后的预测：一项国际，多中心，队列研究

背景

特发性肺纤维化（IPF）的临床过程是不可预测的。临床预测工具不够准确，无法预测疾病的结果。

方法

我们在耶鲁大学（美国康涅狄格州纽黑文市），伦敦帝国学院（英国伦敦），芝加哥大学（美国伊利诺伊州芝加哥），匹兹堡大学（匹兹堡，美国宾夕法尼亚州），弗赖堡大学（德国弗赖堡）和布莱根妇女医院-哈佛医学院（美国马萨诸塞州波士顿）。在4-6年的随访中，从基线收集了425名参与者和98名患者（23％）的外周血单核细胞或全血。通过nCounter分析系统在四个队列中测量了52个基因的标记，并从另外两个队列的微阵列数据（GeneChip）中提取了该基因。我们使用分子亚表型评分算法（SAMS）根据52个基因的特征将患者分为低风险组或高风险组。我们使用竞争风险模型研究了死亡率，并使用Cox比例风险模型研究了无移植生存率。我们使用线性混合效应模型分析了时程数据和对抗纤维化药物的反应。

发现

将SAMS应用于52个基因的特征识别出两组IPF患者（低风险和高风险），在六个队列中的每个队列中，死亡率或无移植生存率均存在显着差异（危险比[HR]范围2·03–4·37）。汇总数据显示死亡率（HR 2·18，95％CI 1·53–3·09; p <0·0001）或无移植存活率（2·04，1·52–2·74; p < 0·0001）。在性别，年龄和生理学指标中添加52个基因的风险状况可显着提高其死亡率的预测准确性。SAMS评分的时间变化与两个队列的强迫肺活量（FVC）变化相关。未经治疗的患者并未随时间改变其风险状况。上分数升高和下分数降低的同时预示着无移植生存期的降低（3·18、1·16-8·76；p = 0·025）。在开始抗纤维化药物治疗后，up得分同时下降和down得分增加与耶鲁大学队列研究者的FVC显着改善（p = 0·0050）有关。

解释

外周血52基因表达特征可预测IPF患者的预后。前瞻性研究应确定52基因签名在预测对治疗反应中的潜在价值。

资金

肺纤维化基金会，罗伯特·伍德·约翰逊基金会的哈罗德·阿莫斯医学系发展计划，以及美国国立卫生研究院的国家心脏，肺和血液研究所。