Noninvasive monitoring of infection and rejection after lung transplantation

Abstract

The survival rate following lung transplantation is among the lowest of all solid-organ transplants, and current diagnostic tests often fail to distinguish between infection and rejection, the two primary posttransplant clinical complications. We describe a diagnostic assay that simultaneously monitors for rejection and infection in lung transplant recipients by sequencing of cell-free DNA (cfDNA) in plasma. We determined that the levels of donor-derived cfDNA directly correlate with the results of invasive tests of rejection (area under the curve 0.9). We also analyzed the nonhuman cfDNA as a hypothesis-free approach to test for infections. Cytomegalovirus is most frequently assayed clinically, and the levels of CMV-derived sequences in cfDNA are consistent with clinical results. We furthermore show that hypothesis-free monitoring for pathogens using cfDNA reveals undiagnosed cases of infection, and that certain infectious pathogens such as human herpesvirus (HHV) 6, HHV-7, and adenovirus, which are not often tested clinically, occur with high frequency in this cohort.

Keywords: cell-free DNA; diagnosis; infection; organ transplantation; rejection.

Conflict of interest statement

The authors declare no conflict of interest.

Figures

Fig. 1.

Patient recruitment, sample collection, and comparison with clinical indicators of rejection and infection. We performed a prospective cohort study to evaluate the performance of the GTD cell-free DNA assay after lung transplantation with simultaneous infection monitoring. Fifty-one patients were recruited (44 bilateral and 7 single-lung) while awaiting transplantation, and 398 plasma samples were collected longitudinally at prespecified intervals posttransplant. For each sample, human- and nonhuman-derived cell-free DNA sequences were identified and analyzed with respect to clinical metrics of graft rejection (108 transbronchial biopsies, tests of pulmonary function, and diagnosis of CLAD and AMR) and infection, respectively.

Fig. 2.

Early and late posttransplant cell-free DNA dynamics and organ-specific features. (A) Fraction of donor-derived cell-free DNA during the first 2 mo posttransplant for rejection-free lung transplant recipients. Donor cfDNA levels were higher for lung than for heart transplant recipients. (Inset) Data for heart transplants from ref. . (B) Donor cfDNA levels measured for single- and double-lung transplants throughout the posttransplant course in rejection-free patients. Solid lines are fits (local polynomial regression, smoothing parameter alpha 0.75; gray shading indicates 90% confidence interval). (C) Estimate of graft cell-decay rate from measurements of the fraction of donor cfDNA. Data are shown as boxplots. The bottom and top of the box indicate the upper and lower quartile, the band inside the box indicates the median.

Fig. S1.

Model fits of cfdDNA early dynamics. (A) Comparison of double- (blue line) and single-exponential (black line) fit of cfdDNA levels recorded for lung transplant recipients as a function of time posttransplant (blue dots). The double-exponential fit is better at capturing the time dynamics at later time points. (B) Weighted residuals (weighted by the square of the cfdDNA levels) for the single- (black line) and double-exponential model (blue line) as function of time posttransplant. The R-squared measure of the goodness of the fit with weighted residuals is 0.89 for the double-exponential model and 0.47 for the single-exponential model.

Fig. 3.

Cell-free donor DNA at rejection. (A) Donor cfDNA level measured for patients diagnosed with a moderate or severe acute rejection event, with vertical lines indicating rejection diagnosis via transbronchial biopsy. (B) Overview of all data collected for 51 lung transplant recipients (n = 398 samples) highlighting points measured in the absence (blue) and presence (red) of clinical signs of rejection. Data collected during the first 2 mo posttransplant are shown in gray, with early time dynamics excluded from signal analysis.

Fig. 4.

Analysis of the performance of cfdDNA as a marker for lung transplant rejection. (A) cfdDNA levels by transbronchial biopsy score. (B) ROC analysis of the performance of cfdDNA in classifying rejection-free versus rejecting patients (black line, A0 vs. A3–A4, moderate-to-severe rejection, AUC 0.9). Also shown are ROC analyses of the test performance in distinguishing rejection-free patients and patients with mild-to-severe rejection (red line, A0 vs. A2–A4, AUC 0.76) and the performance in distinguishing rejection-free and patients with minimal-to-severe rejection (blue line, A0 vs. A1–A4, AUC 0.7). (C) cfdDNA levels versus FEV1 level (% predicted given age, sex, and body composition). (D–G) cfdDNA levels by (D) the presence or absence of clinical signs of acute rejection, (E) decision to treat for acute rejection, (F) CLAD, and (G) clinical signs of AMR. Data are shown as boxplots in A, D–G. The bottom and top of the box indicate the upper and lower quartile, the band inside the box indicates the median.

Fig. 5.

CMV infection-induced allograft injury. (A) Correlation between clinical report of CMV (HHV-5, BAL, and serum) and donor cfdDNA signal matched to clinical test date (P values, Mann–Whitney U test). Data are shown as boxplots. The bottom and top of the box indicate the upper and lower quartile, the band inside the box indicates the median. (B) P values for the correlation between clinical diagnosis of infection and cell-free DNA level (dashed line indicates the Bonferroni-corrected significance threshold, P = 0.05/31) for infections with more than one clinical positive test result (31 infections). (C) ROC curve that tests the performance of CMV-derived cell-free DNA level in CMV-positive and CMV-negative patients (AUC 0.91).

Fig. S2.

Statistics for collected clinical data on infections. (A) The number of each clinical test type performed. There are 28 different tests performed on the cohort, although 13 are performed only once and CMV PCR represents approximately half the total number of tests performed. (B) Clinical tests were performed on 14 different body fluids, with the majority of tests (∼64%) performed on serum. For each test, we determined the number of unique infectious agents that were measured. In some cases, the measured infection is obvious based upon the test type (e.g., for quantitative PCR tests for a given pathogen). For culture assays, we determined the total number of infectious agents cultured in our cohort (46 unique organisms cultured). Assuming these 46 infections to be detectable for each culture performed, a total of ∼36,000 measurements for specific infections was performed on our cohort. The majority (∼98.5%) of these measurements are negative. Of the 540 positive test results, CMV is the most common.

Fig. 6.

Monitoring the infectome. (A) Clinical testing frequency compared with the incidence of viral infections detected in sequencing. (B) Time-series data for patients who tested positive (red arrows) for specific infections relative to those who were not tested. (a) Coverage ratio for adenovirus in L78 with clinical positives highlighted relative to an untested patient (L34). (b) Coverage ratio for polyomavirus in L69 with one positive test relative to sustained signal in an untested patient (L57). (c) Three herpesvirus infections (HHV-4, -5, and -8) in L58 with both positive (red) and negative (black) tests for CMV (HHV-5) highlighted. (d) Coverage ratio for microsporidia in I6, with four positive tests shown, relative to the signal observed in patient L78, who had symptoms of microsporidiosis but was not tested. In all cases, the logged coverage ratio (organism coverage relative to human coverage for the sample) is reported. Because the data are logged, zero values were replaced with the detection limit of the assay (horizontal lines indicate the coverage ratio obtained for a single read assigned to the organism in the given sample).

Fig. S3.

Correlations with infections detected at various body sites. (A) ROC curve and time-series data for Klebsiella pneumoniae (687 negative results and 13 positive clinical tests). All positive test results are for patient L24, and (except for one for BAL) are detected in urine. (B) Matched sample–test data for Aspergillus niger (481 clinical negatives, 2 clinical positives). Both positives are in L17 (one BAL and one sputum). The single test for BAL is shown. The percentile plot (Left) indicates that measurements in L24 are high relative to the cohort. (C) Correlations with clinical tests based upon both infection type and body fluid queried by the clinical assay. For each category, the measured cell-free DNA signal is aggregated across all infections and partitioned based upon the result of the matched clinical test, and P values are computed using Mann–Whitney U test.