Clinical trial NCT05260372: Next Generation Sequencing to Detect Acute Rejection in Lung Transplant Patients.
Official title: Next Generation Sequencing for the Diagnosis of Acute Rejection in Patients Undergoing Lung Transplantation. Pilot Study for Comparative Longitudinal Descriptive Trial.
Study type: OBSERVATIONAL
Overall status: UNKNOWN
Conditions: Lung Transplant Rejection
Keywords: Acute rejection
Donor-derived cell-free DNA
Lung transplantation
Next-generation sequencing
Lead sponsor: Fondazione IRCCS Ca' Granda, Ospedale Maggiore Policlinico
Sponsor class: OTHER
Collaborators: name: University of Milan
First posted: 2022-03-02
Enrollment: 55
Sex: ALL
Minimum age (years): 18
Maximum age (years): 65
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: PROBABILITY_SAMPLE
Study population: Patients undergoing lung transplantation at Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy

Brief summary: Lung transplantation is a consolidated treatment in selected patients with end-stage respiratory diseases; however, acute rejection remains an important cause of lung allograft loss and a risk factor for chronic allograft dysfunction. Histopathological examination of lung tissue is the gold standard for the diagnosis of acute rejection, therefore recipients undergo surveillance transbronchial biopsy and bronchoalveolar lavage after transplantation. However, the obtained tissue is sometimes inadequate for histopathology, and the endoscopic procedure can lead to complications (bleeding, pneumothorax). The quantification of donor-derived cell-free DNA (ddcfDNA) in the recipient plasma has shown to be increased in case of acute rejection, and could represent an early and non-invasive diagnostic marker to detect acute rejection. We planned to enroll all patients aged 18 to 65 years old enlisted for lung transplantation at our centre. Patients undergoing retransplantation and patients with a history of prior solid organ transplantation were excluded. The quantification of donor-derived cell-free DNA was performed 15 days and 3, 6, and 12 months after transplantation, concurrently with the routine surveillance bronchoscopies as per our protocol; the same analysis was also conducted in case of suspected clinical rejection.

Detailed description: The purpose of the study is the identification of a non-invasive post-transplantation acute lung rejection monitoring method, by identification of donor derived cell free DNA (ddcf-DNA) in the recipient's plasma. At the time of transplantation, the ddcf-DNA derived from lymphocytes present in the peripheral blood of the donor and recipient will be genotyped by analysis of about 300 single nucleotide polymorphisms (SNPs) with a high probability of being differently homozygous between donor and recipient, according to the frequencies in the population reported on genomic databases. During the post-transplant follow-up, the presence of ddcf-DNA will be monitored by identification of the donor's SNP in the free DNA extracted from the recipient's plasma using the next-generation sequencing (NGS) method. Levels of ddcfDNA in the recipient will be correlated with clinical and histological data obtained from routine 3, 6 and 12 month post-transplant surveillance lung biopsies. All patients enrolled in the study will sign a study-specific written informed consent.

-Timeline: T-1 (before lung transplantation): DNA genotyping of the recipient; T0 (lung transplantation): genotyping of donor DNA; T1, T2, T3, T4 (15 days, 3, 6, 12 months after lung transplantation): ddcfDNA research on recipient serum; Tr: in any case of clinical suspicion of acute cellular rejection. At T2, T3, T4 and Tr, surveillance trans-bronchial lung biopsies (TBBs) are also performed, as per our centre's standard post-transplant lung follow-up protocol.

-Sample size and statistical analysis: Proportion of ddcfDNA/recipient DNA: stable patients (pA): 0.01, patients with graft damage (pB): 0.30, proportion nA/nB: 3, Power (1-beta): 0.80, Alpha: 5%, Margin of non inferiority δ: 0.05. Population (A+B) : 48; for possible exit from the study we add 7 cases for a total of 55 patients.

Eligibility criteria: Inclusion Criteria:

* Patients undergoing lung transplantation at our centre
* Patients aged 18 to 65 years old
* Written informed consent to partecipate in the study

Exclusion Criteria:

* Patients undergoing retransplantation
* Patients with a history of prior solid organ transplantation

Primary outcomes:
measure: Proportion ddcfDNA
timeFrame: 1 year
Secondary outcomes:
measure: ddcfDNA-clinical data correlation
timeFrame: 1 year
Other outcomes:
Locations:
city: Milan
country: Italy
facility: Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico
Study officials:
affiliation: Fondazione IRCCS Ca' Granda, Ospedale Maggiore Policlinico
name: Mario Nosotti, MD, Professor
role: PRINCIPAL_INVESTIGATOR
References:
citation: Report CRT, Italy, Rete nazionale trapianti, 2017
citation: Chambers DC, Yusen RD, Cherikh WS, Goldfarb SB, Kucheryavaya AY, Khusch K, Levvey BJ, Lund LH, Meiser B, Rossano JW, Stehlik J; International Society for Heart and Lung Transplantation. The Registry of the International Society for Heart and Lung Transplantation: Thirty-fourth Adult Lung And Heart-Lung Transplantation Report-2017; Focus Theme: Allograft ischemic time. J Heart Lung Transplant. 2017 Oct;36(10):1047-1059. doi: 10.1016/j.healun.2017.07.016. Epub 2017 Jul 19. No abstract available.
citation: Lande JD, Patil J, Li N, Berryman TR, King RA, Hertz MI. Novel insights into lung transplant rejection by microarray analysis. Proc Am Thorac Soc. 2007 Jan;4(1):44-51. doi: 10.1513/pats.200605-110JG.
citation: Tosi D, Carrinola R, Morlacchi LC, Tarsia P, Rossetti V, Mendogni P, Rosso L, Righi I, Damarco F, Nosotti M. Surveillance Transbronchial Biopsy Program to Evaluate Acute Rejection After Lung Transplantation: A Single Institution Experience. Transplant Proc. 2019 Jan-Feb;51(1):198-201. doi: 10.1016/j.transproceed.2018.04.073. Epub 2018 Jun 28.
citation: Rademacher J, Suhling H, Greer M, Haverich A, Welte T, Warnecke G, Gottlieb J. Safety and efficacy of outpatient bronchoscopy in lung transplant recipients - a single centre analysis of 3,197 procedures. Transplant Res. 2014 May 27;3:11. doi: 10.1186/2047-1440-3-11. eCollection 2014.
citation: De Vlaminck I, Martin L, Kertesz M, Patel K, Kowarsky M, Strehl C, Cohen G, Luikart H, Neff NF, Okamoto J, Nicolls MR, Cornfield D, Weill D, Valantine H, Khush KK, Quake SR. Noninvasive monitoring of infection and rejection after lung transplantation. Proc Natl Acad Sci U S A. 2015 Oct 27;112(43):13336-41. doi: 10.1073/pnas.1517494112. Epub 2015 Oct 12.
citation: Agbor-Enoh S, Jackson AM, Tunc I, Berry GJ, Cochrane A, Grimm D, Davis A, Shah P, Brown AW, Wang Y, Timofte I, Shah P, Gorham S, Wylie J, Goodwin N, Jang MK, Marishta A, Bhatti K, Fideli U, Yang Y, Luikart H, Cao Z, Pirooznia M, Zhu J, Marboe C, Iacono A, Nathan SD, Orens J, Valantine HA, Khush K. Late manifestation of alloantibody-associated injury and clinical pulmonary antibody-mediated rejection: Evidence from cell-free DNA analysis. J Heart Lung Transplant. 2018 Jul;37(7):925-932. doi: 10.1016/j.healun.2018.01.1305. Epub 2018 Jan 31.
citation: De Vlaminck I, Valantine HA, Snyder TM, Strehl C, Cohen G, Luikart H, Neff NF, Okamoto J, Bernstein D, Weisshaar D, Quake SR, Khush KK. Circulating cell-free DNA enables noninvasive diagnosis of heart transplant rejection. Sci Transl Med. 2014 Jun 18;6(241):241ra77. doi: 10.1126/scitranslmed.3007803.
citation: Rizopoulos, D. Joint Models for Longitudinal and Time-to-Event Data, with Applications in R. Boca Raton: Chapman & Hall/CRC. 2012.
citation: Wu, L. Mixed Effects Models for Complex Data. Boca Raton: Chapman & Hall/CRC, Chapter 8. 2009
citation: Rizopoulos, D. The R package JMbayes for fitting joint models for longitudinal and timeto-event data using MCMC. Journal of Statistical Software 72(7), 1-45. doi:10.18637/jss.v072.i07. 2016
citation: Rizopoulos D. Dynamic predictions and prospective accuracy in joint models for longitudinal and time-to-event data. Biometrics. 2011 Sep;67(3):819-29. doi: 10.1111/j.1541-0420.2010.01546.x. Epub 2011 Feb 9.
citation: R Foundation for Statistical Computing, Vienna, Austria(2015)