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WO 2015/035367 Al 12 March 2015 (12.03.2015) P O P C T

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WO 2015/035367 Al 12 March 2015 (12.03.2015) P O P C T (12) INTERNATIONAL APPLICATION PUBLISHED UNDER THE PATENT COOPERATION TREATY (PCT) (19) World Intellectual Property Organization International Bureau (10) International Publication Number (43) International Publication Date WO 2015/035367 Al 12 March 2015 (12.03.2015) P O P C T (51) International Patent Classification: (72) Inventors; and C12Q 1/68 (2006.01) (71) Applicants : SALOMON, Daniel [US/US]; 4728 Reedley Terrace, San Diego, CA 92130 (US). KURIAN, Sunil, M. (21) International Application Number: [IN/US]; 10963 Tobago Road, San Diego, CA 92126 (US). PCT/US2014/054735 HEAD, Steven [US/US]; 11673 Valle Vista Road, (22) International Filing Date: Lakeside, CA 92040 (US). ABECASSIS, Michael, M. >September 2014 (09.09.2014) [US/US]; 1975 Keats Court, Highland Park, IL 60035 (US). FRIEDEWALD, John, J. [US/US]; 3315 N . Lake- (25) Filing Language: English wood Avenue, Chicago, IL 60657 (US). LEVITSKY, (26) Publication Language: English Josh [US/US]; 1308 Judson, Evanston, IL 60201 (US). (30) Priority Data: (74) Agents: FITTING, Thomas et al; The Scripps Research 61/875,276 ' September 2013 (09.09.2013) US Institute, 10550 North Torrey Pines Road, TPC-8, La Jolla, 61/965,040 16 January 2014 (16.01.2014) US CA 92037 (US). 62/001,889 22 May 2014 (22.05.2014) US (81) Designated States (unless otherwise indicated, for every 22 May 2014 (22.05.2014) US 62/001,909 kind of national protection available): AE, AG, AL, AM, 62/001,902 22 May 2014 (22.05.2014) US AO, AT, AU, AZ, BA, BB, BG, BH, BN, BR, BW, BY, 62/029,038 25 July 2014 (25.07.2014) US BZ, CA, CH, CL, CN, CO, CR, CU, CZ, DE, DK, DM, (71) Applicants: THE SCRIPPS RESEARCH INSTITUTE DO, DZ, EC, EE, EG, ES, FI, GB, GD, GE, GH, GM, GT, [US/US]; 10550 North Torrey Pines Road, La Jolla, CA HN, HR, HU, ID, IL, IN, IR, IS, JP, KE, KG, KN, KP, KR, 92037 (US). NORTHWESTERN UNIVERSITY KZ, LA, LC, LK, LR, LS, LU, LY, MA, MD, ME, MG, [US/US]; 633 Clark Street, Evanston, IL 60208 (US). MK, MN, MW, MX, MY, MZ, NA, NG, NI, NO, NZ, OM, PA, PE, PG, PH, PL, PT, QA, RO, RS, RU, RW, SA, SC, [Continued on nextpage] (54) Title: METHODS AND SYSTEMS FOR ANALYSIS OF ORGAN TRANSPLANTATION Fig. 1 (57) Abstract: Disclosed herein are methods of detecting, predicting or monitoring a status or outcome of a transplant in a transplant recipient. In some aspects, a method for detecting or predicting a condition of a transplant recipient comprises a) 110. Obtain sample from a obtaining a sample, wherein the sample comprises one or transplant recipient more gene expression products from the transplant recipient; b) performing an assay to determine an expression level of the one or more gene expression products from the transplant re cipient; and c) detecting or predicting the condition of the transplant recipient by applying an algorithm to the expres 120. Perform assay to sion level determined in step (b), wherein the algorithm is a determine gene expression classifier capable of distinguishing between at least two con level ditions that are not normal conditions, and wherein one of the at least two conditions is transplant rejection or transplant dysfunction. 130. Apply computer algorithm to tire gene expression level 140. Classification Class A Class C based on the results © o o w o 2015/035367 AI Iinn iiiiiii 1mil i mil il i 1ill inn i l i i i imill i i i SD, SE, SG, SK, SL, SM, ST, SV, SY, TH, TJ, TM, TN, DK, EE, ES, FI, FR, GB, GR, HR, HU, IE, IS, IT, LT, TR, TT, TZ, UA, UG, US, UZ, VC, VN, ZA, ZM, ZW. LU, LV, MC, MK, MT, NL, NO, PL, PT, RO, RS, SE, SI, SK, SM, TR), OAPI (BF, BJ, CF, CG, CI, CM, GA, (84) Designated States (unless otherwise indicated, for every GN, GQ, GW, KM, ML, MR, NE, SN, TD, TG). kind of regional protection available): ARIPO (BW, GH, GM, KE, LR, LS, MW, MZ, NA, RW, SD, SL, ST, SZ, Published: TZ, UG, ZM, ZW), Eurasian (AM, AZ, BY, KG, KZ, RU, — with international search report (Art. 21(3)) TJ, TM), European (AL, AT, BE, BG, CH, CY, CZ, DE, METHODS AND SYSTEMS FOR ANALYSIS OF ORGAN TRANSPLANTATION CROSS REFERENCE TO RELATED APPLICATIONS [0002] This application claims the benefit of U.S. Provisional Application No. 61/875,276 filed on September 9, 2013, U.S. Provisional Application No. 61/965,040 filed on January 16, 2014, U.S. Provisional Application No. 62/001,889 filed on May 22, 2014, U.S. Provisional Application No. 62/029,038 filed on July 25, 2014, U.S. Provisional Application No. 62/001,909 filed on May 22, 2014, and U.S. Provisional Application No. 62/001,902 filed on May 22, 2014, all of which are incorporated herein by reference in their entireties. GOVERNMENT RIGHTS [0003] This invention was made with government support under Grant Numbers AI052349, AI084146, and AI063603 awarded by the National Institutes of Health. The government has certain rights in the invention. BACKGROUND [0004] The current method for detecting organ rejection in a patient is a biopsy of the transplanted organ. However, organ biopsy results can be inaccurate, particularly if the area biopsied is not representative of the health of the organ as a whole (e.g., as a result of sampling error). There can be significant differences between individual observors when they read the same biopsies independently and these discrepancies are particularly an issue for complex histologies that can be challenging for clinicians. Biopsies, especially surgical biopsies, can also be costly and pose significant risks to a patient. In addition, the early detection of rejection of a transplant organ may require serial monitoring by obtaining multiple biopsies, thereby multiplying the risks to the patients, as well as the associated costs. [0005] Transplant rejection is a marker of ineffective immunosuppression and ultimately if it cannot be resolved, a failure of the chosen therapy. The fact that 50% of kidney transplant patients will lose their grafts by ten years post transplant reveals the difficulty of maintaining adequate and effective longterm immunosuppression. There is a need to develop a minimally invasive, objective metric for detecting, identifying and tracking transplant rejection. In particular, there is a need to develop a minimally invasive metric for detecting, identifying and tracking transplant rejection in the setting of a confounding diagnosis, such as acute dysfunction with no rejection. This is especially true for identifying the rejection of a transplanted kidney. For example, elevated creatinine levels in a kidney transplant recipient may indicate either that the patient is undergoing an acute rejection or acute dysfunction without rejection. A minimally- invasive test that is capable of distinguishing between these two conditions would therefore be extremely valuable and would diminish or eliminate the need for costly, invasive biopsies. SUMMARY [0006] The methods and systems disclosed herein may be used for detecting or predicting a condition of a transplant recipient (e.g., acute transplant rejection, acute dysfunction without rejection, subclinical acute rejection, hepatitis C virus recurrence, etc.). In some aspects, a method for detecting or predicting a condition of a transplant recipient comprises a) obtaining a sample, wherein the sample comprises one or more gene expression products from the transplant recipient; b) performing an assay to determine an expression level of the one or more gene expression products from the transplant recipient; and c) detecting or predicting the condition of the transplant recipient by applying an algorithm to the expression level determined in step (b), wherein the algorithm is a classifier capable of distinguishing between at least two conditions that are not normal conditions, and wherein one of the at least two conditions is transplant rejection or transplant dysfunction. In another embodiment, a method for detecting or predicting a condition of a transplant recipient comprises a) obtaining a sample, wherein the sample comprises one or more gene expression products from the transplant recipient; b) performing an assay to determine an expression level of the one or more gene expression products from the transplant recipient; and c) detecting or predicting the condition of the transplant recipient by applying an algorithm to the expression level determined in step (b), wherein the algorithm is a classifier capable of distinguishing between at least two conditions that are not normal conditions, and wherein one of the at least two conditions is transplant rejection. In another embodiment, a method for detecting or predicting a condition of a transplant recipient comprises a) obtaining a sample, wherein the sample comprises one or more gene expression products from the transplant recipient; b) performing an assay to determine an expression level of the one or more gene expression products from the transplant recipient; and c) detecting or predicting the condition of the transplant recipient by applying an algorithm to the expression level determined in step (b), wherein the algorithm is a classifier capable of distinguishing between at least two conditions that are not normal conditions, and wherein one of the at least two conditions is transplant dysfunction. In some cases, the transplant recipient is a kidney transplant recipient. In some cases, the transplant recipient is a liver transplant recipient. [0007] In some embodiments, a method of detecting or predicting a condition of a transplant recipient comprises: a) obtaining a sample, wherein the sample comprises one or more gene expression products from the transplant recipient; b) performing an assay to determine an expression level of the one or more gene expression products from the transplant recipient; and c) detecting or predicting the condition of the transplant recipient by applying an algorithm to the expression level determined in step (b), wherein the algorithm is capable of distinguishing between acute rejection and transplant dysfunction with no rejection.
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