Validation of a 52-gene risk profile for outcome prediction in patients with idiopathic pulmonary fibrosis: an international, multicentre, cohort study

Abstract

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.

Figure 1. Study design

The outline summarizes the (a) time to event and (b) time course analysis design for this study including the cohorts, blood compartments, experiments and statistical methods used in each independent cohort and in the pooled data analysis. PBMC: Peripheral blood mononuclear cells. BWH-HMS: Brigham and Women’s Hospital-Harvard Medical School. Dates of enrollment for each cohort are included in figure 1a. For figure 1b, time is presented in years (average and range, in parenthesis).

Figure 2. 52-gene risk profiles are predictive of outcome in IPF

(a) Clustering of IPF patients based on52-gene risk profiles (high vs low) derived using SAMS in each one of the six cohorts studied. Every row represents a gene and every column a patient. Color scale is shown adjacent to heat maps in log-based two scale; yellow denotes increase over the geometric mean of samples and purple, decrease. (b) Mortality and Transplant-free survival (TFS) differs between high vs low risk profiles based on the 52-gene signature in each independent cohort.

Figure 3. 52-gene risk profiles are predictive of outcome independent of demographic and clinical variables

(a) Pooled data analysis comparing high vs low risk profile patients from all cohorts. Color scale is shown adjacent to heat maps in log-based two scale. (b) Mortality and (c) Transplant-free survival (TFS) differs between high vs low risk patients from all cohorts after adjusting for age, gender, FVC% and immunosuppressive therapy. (d) Area Under the Curve (AUC) of time-dependent ROC analysis for (d) mortality and (e)TFS based on the GAP index alone or the G-GAP index in all patients.

Figure 4. 52-gene signature trends over time demonstrate association with disease progression and survival

(a) up and down (b) scores from SAMS, and (c) FVC volumes do not shift their trends over time in high (continuous red line) vs low (continuous black line) risk groups (Pittsburgh cohort). Pointwise confidence intervals are represented in purple. (d) Bidirectional changes in SAMS scores (Simultaneous increase in up score and decrease in down score) can be observed during disease course in IPF and are more prominent in high risk individuals (example shown in dotted black line box). (e) Bidirectional changes in SAMS scores are predictive of Transplant-free survival (TFS). Dotted blue line (high risk) –Pittsburgh cohort patients with 30-day bidirectional changes in SAMS scores ≥10%. Continuous red line (low risk) – Pittsburgh cohort patients with 30-day bidirectional changes in SAMS scores <10%. Results adjusted by age, gender, FVC and immunosuppressive therapy.