Method for identifying kidney allograft recipients at risk for chronic injury
AssigneesIcahn School of Medicine at Mount Sinai, Western Sydney Local Health District
InventorsBarbara Murphy, Weijia Zhang, Philip J. O`Connell
Life of this patent
Estimating the term…
Abstract
A method for identifying a renal allograft recipient at risk for chronic allograft damage or interstutial fibrosis and tubular atrophy (IF/TA) by comparing the transcription level of a preselected gene signature set with the transcription level of a comparison standard, and diagnosing the recipient as being at risk for chronic allograft damage if the transcription level of the preselected gene signature set is significantly higher than the transcription level of the comparison standard.
Claims13
1-28. (canceled)
A method for treating a human renal allograft recipient at risk for chronic allograft damage or interstitial fibrosis and tubular atrophy (IF/TA) comprising the steps of:
- (a) identifying a human renal allograft recipient as being at risk for chronic allograft damage or IF/TA upon detection of transcription levels in a tissue specimen from the renal allograft recipient of a preselected gene signature set which are higher than the transcription levels of the preselected gene signature set in a control sample obtained from a subject that did not develop chronic allograft damage or IF/TA, said transcription levels obtained by
- i. synthesizing cDNA from mRNA isolated from a biopsy specimen obtained from said renal allograft recipient, and
- ii. detecting the transcription levels of the preselected gene signature set in the cDNA, wherein said gene signature set comprises the genes KLHL13, KAAG1, MET, SPRY4, SERINC5, CHCHD10, FJX1, WNT9A, RNF149, ST5, TGIF1, RXRA and ASB15;
- and
- (b) administering to the human renal allograft recipient having higher transcription levels of the preselected gene signature set an effective amount for treating chronic allograft damage, of an anti-fibrotic agent, an immunosuppressive agent, or both.
Dependencies are inferred from the claim text.