BACKGROUND: We have previously demonstrated that a peripheral blood transcriptional profile using 11 distinct genes predicts onset of cardiac allograft rejection weeks to months prior to the actual event. METHODS: In this analysis, we ascertained the performance of this transcriptional algorithm in a Bayesian representative population: 28 cardiac transplant recipients who progressed to moderate to severe rejection; 53 who progressed to mild rejection; and 46 who remained rejection-free. Furthermore, we characterized longitudinal alterations in the transcriptional gene expression profile before, during and after recovery from rejection. RESULTS: In this patient cohort, we found that a gene expression score (range 0 to 40) of <or =20 represents very low risk of rejection in the subsequent 12 weeks: 0 progressed to treatable (ISHLT Grade > or =3A) rejection; 16 of 53 (30%) from the intermediate group (those who progressed to ISHLT Grade 1B or 2) and 13 of 46 (28%) controls (who remained Grade 0 or 1A) had scores < or =20. A gene score of > or =30 was associated with progression to moderate to severe rejection in 58% of cases. These two extreme scores (< or =20 or > or =30) represented 44% of the cardiac transplant population within 6 months post-transplant. In addition, longitudinal gene expression analysis demonstrated that baseline scores were significantly higher for those who went on to reject, remained high during an episode of rejection, and dropped post-treatment for rejection (p < 0.01). CONCLUSIONS: The use of gene expression profiling early after transplantation allows for separation into low-, intermediate- or high-risk categories for future rejection, permitting development of discrete surveillance strategies.
BACKGROUND: We have previously demonstrated that a peripheral blood transcriptional profile using 11 distinct genes predicts onset of cardiac allograft rejection weeks to months prior to the actual event. METHODS: In this analysis, we ascertained the performance of this transcriptional algorithm in a Bayesian representative population: 28 cardiac transplant recipients who progressed to moderate to severe rejection; 53 who progressed to mild rejection; and 46 who remained rejection-free. Furthermore, we characterized longitudinal alterations in the transcriptional gene expression profile before, during and after recovery from rejection. RESULTS: In this patient cohort, we found that a gene expression score (range 0 to 40) of <or =20 represents very low risk of rejection in the subsequent 12 weeks: 0 progressed to treatable (ISHLT Grade > or =3A) rejection; 16 of 53 (30%) from the intermediate group (those who progressed to ISHLT Grade 1B or 2) and 13 of 46 (28%) controls (who remained Grade 0 or 1A) had scores < or =20. A gene score of > or =30 was associated with progression to moderate to severe rejection in 58% of cases. These two extreme scores (< or =20 or > or =30) represented 44% of the cardiac transplant population within 6 months post-transplant. In addition, longitudinal gene expression analysis demonstrated that baseline scores were significantly higher for those who went on to reject, remained high during an episode of rejection, and dropped post-treatment for rejection (p < 0.01). CONCLUSIONS: The use of gene expression profiling early after transplantation allows for separation into low-, intermediate- or high-risk categories for future rejection, permitting development of discrete surveillance strategies.
Authors: A M K Kaul; S Goparaju; N Dvorina; S Iida; K S Keslar; C A de la Motte; A Valujskikh; R L Fairchild; W M Baldwin Journal: Am J Transplant Date: 2015-01-12 Impact factor: 8.086
Authors: Irina A Ionova; Jeannette Vásquez-Vivar; Brian C Cooley; Ashwani K Khanna; Jennifer Whitsett; Anja Herrnreiter; Raymond Q Migrino; Zhi-Dong Ge; Kevin R Regner; Keith M Channon; Nicholas J Alp; Galen M Pieper Journal: Am J Physiol Heart Circ Physiol Date: 2010-04-23 Impact factor: 4.733
Authors: J Perren Cobb; Ernest E Moore; Doug L Hayden; Joseph P Minei; Joseph Cuschieri; Jingyun Yang; Qing Li; Nan Lin; Bernard H Brownstein; Laura Hennessy; Philip H Mason; William S Schierding; David J Dixon; Ronald G Tompkins; H Shaw Warren; David A Schoenfeld; Ronald V Maier Journal: Ann Surg Date: 2009-10 Impact factor: 12.969
Authors: Dominick Sinicropi; Kunbin Qu; Francois Collin; Michael Crager; Mei-Lan Liu; Robert J Pelham; Mylan Pho; Andrew Dei Rossi; Jennie Jeong; Aaron Scott; Ranjana Ambannavar; Christina Zheng; Raul Mena; Jose Esteban; James Stephans; John Morlan; Joffre Baker Journal: PLoS One Date: 2012-07-13 Impact factor: 3.240
Authors: Steven E Lipshultz; Jayanthi J Chandar; Paolo G Rusconi; Alessia Fornoni; Carolyn L Abitbol; George W Burke; Gaston E Zilleruelo; Si M Pham; Elena E Perez; Ruchika Karnik; Juanita A Hunter; Danielle D Dauphin; James D Wilkinson Journal: Clinics (Sao Paulo) Date: 2014 Impact factor: 2.365