Methods for diagnosing risk of renal allograft fibrosis and rejection

Patent metadata

Field	Value
Publication number	US-10308985-B2
Application number	US-201515320208-A
Country	US
Kind code	B2
Filing date	Jun 26, 2015
Priority date	Jun 26, 2014
Publication date	Jun 4, 2019
Grant date	Jun 4, 2019

Abstract

Disclosed herein is a method for diagnosing a renal allograft recipient's risk for developing fibrosis of the allograft and allograft loss. The method includes determining the expression levels of certain microRNAs, which have been determined to be predictive of an allograft recipient's risk. Also disclosed herein is a method of treating a renal allograft recipient to inhibit fibrosis of the allograft and allograft loss, as well as kits for use in the methods disclosed herein.

First claim

What is claimed is:

1. A method for diagnosing and treating a renal allograft recipient at risk for developing fibrosis of the allograft and allograft loss, the method comprising:
(a) determining the expression levels of said microRNAs in a blood sample using Nanostring analysis, wherein the microRNAs are hsa-mir-128, hsa-mir-29b-3p, hsa-mir-302b-3p, and hsa-mir-192-5p;
(b) determining the expression levels of said microRNAs in a control sample using Nanostring analysis for each microRNA;
(c) diagnosing the recipient as being at risk for developing fibrosis of the allograft and allograft loss if the expression levels of said miRNA samples are altered relative to a control level for each microRNA, and
(d) treating the allograft recipient by administering an effective amount of a drug for treating fibrosis of the allograft and allograft loss.

2. The method of claim 1 , further comprising
diagnosing the recipient as being at high risk for developing fibrosis of the allograft and allograft loss if the expression levels of hsa-miR-128 and hsa-miR-302b-3p are increased relative to a control level for each microRNA, and the expression levels of hsa-miR-29b-3p and hsa-miR-192-5p are decreased relative to the control level for each microRNA based on the probability score cutoff determined from the training set; wherein said drug is an anti-rejection drug.

3. The method of claim 1 , wherein determining the expression levels comprise
isolating mRNA from the blood sample;
synthesizing cDNA from the mRNA; and
measuring the expression levels of microRNAs hsa-mir-128, hsa-mir-29b-3p, hsa-mir-302b-3p, and hsa-mir-192-5p from the sample.

4. The method of claim 1 , wherein diagnosing the recipient's risk comprises calculating the recipient's risk by applying the expression levels determined in the recipient's sample to a penalized logistic regression fitting model.

5. The method of claim 4 , wherein the penalized logistic regression fitting model from which the risk will be calculated utilizes the formula:

log
⁢

⁢

p
⁡

(
x
)

1
-

p
⁡

(
x
)

=

β

0
+

*

⁢

β
1
*

⁢

g
1

+

β
i
*

⁢

g
i

+
…
+

β
4
*

⁢

g
4

where (p(x) is the probability of developing fibrosis, β* i is penalized coefficiency and g i is the expression value of miRNA i.

6. The method of claim 2 wherein the anti-rejection drug is selected from the group consisting of cyclosporine and Belatacept.

7. The method of claim 6 , which comprises administering between 4 and 6 mg/kg bodyweight/day of cyclosporine to the allograft recipient.

8. The method of claim 2 , wherein the anti-rejection drug is an immunosuppressive or anti-proliferative agent.

9. The method of claim 8 , wherein the immunosuppressive agent is a member selected from the group consisting of a mycophenolate mofetil (MMF), sirolimus, prednisone, Mycophenolate Mofetil, Mycophenolate Sodium and Azathioprine.

10. The method of claim 1 , wherein determining the expression levels of miRNAs hsa-mir-128, hsa-mir-29b-3p, hsa-mir-302b-3p, and hsa-mir-192-5p comprises performing an assay selected from the group consisting of qPCR, microarray, and Nanostring analysis.

11. The method of claim 4 , wherein the Nanostring analysis comprises annealing the cDNA comprising the microRNAs to barcode probes specific for the microRNAs, immobilizing the cDNA, and quantifying the probes bound to the cDNA by digital analyzer.

12. The method of claim 1 , further comprising modifying the immunosuppression regimen of an allograft recipient diagnosed as being at high risk for fibrosis of the allograft and allograft loss.

13. The method of claim 12 , wherein modifying the immunosuppression regimen comprises administering to the recipient an anti-rejection drug selected from the group consisting of Belatacept, rapamycin and Mycophenolate Mofetil.

14. The method of claim 12 , wherein modifying the immunosuppression regimen comprises administering to the recipient an anti-fibrosis drug selected from the group consisting of Pirfenidone, relaxin, Bone morphogenetic protein 7 (BMP-7) and Hepatic growth factor (HGF) 6.

15. The method of claim 1 further comprising diagnosing the recipient as being at low risk for developing fibrosis of the allograft and allograft loss if the expression levels of hsa-miR-128 and hsa-miR-302b-3p are decreased relative to the control level for each microRNA, and the expression levels of hsa-miR-29b-3p and hsa-miR-192-5p are increased relative to the control level for each microRNA based on the probability score cutoff determined from the training set and wherein said drug is an anti-fibrotic agent.

16. The method of claim 15 , further comprising administering an anti-fibrotic agent to the allograft recipient if the recipient is diagnosed as being at low risk for developing fibrosis of the allograft and allograft loss.

17. The method of claim 16 , wherein the anti-fibrotic agent is selected from the group consisting of Pirfenidone, relaxin, Bone morphogenetic protein 7 (BMP-7) and Hepatic growth factor (HGF) 6.

18. The method of claim 1 wherein said anti-rejection drug is rapamycin.

19. The method of claim 15 , further comprising calculating the probability score of fibrosis risk for said recipient using the equation:

log
⁢

⁢

p
⁡

(
x
)

1
-

p
⁡

Assignees

• Icahn School Med Mount Sinai

Inventors

• Murphy Barbara
• Zhang Weijia

Classifications

• G01N2800/347

  Renal failures; Glomerular diseases; Tubulointerstitial diseases, e.g. nephritic syndrome, glomerulonephritis; Renovascular diseases, e.g. renal artery occlusion, nephropathy · CPC title

• C12Q2600/178

  miRNA, siRNA or ncRNA · CPC title

• C12Q2600/118

  Prognosis of disease development · CPC title

• C12Q1/6883 Primary

  for diseases caused by alterations of genetic material · CPC title

• C12Q2600/158

  Expression markers · CPC title