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Despite advances in immunosuppression, T cell-mediated rejection (TCMR) in kidney transplants remains important in the differential diagnosis of graft dysfunction, as the end point of clinical trials of immunosuppressive drugs (ISDs), and as the prototype for T cell-mediated inflammatory diseases . The biopsy diagnosis of TCMR is based on histologic lesions, guided by the recommendations of the Banff self-organizing group . The lesions (interstitial infiltrate, tubulitis, intimal arteritis) were selected at the 1991 meeting based on opinions derived from the case mix at the time: relatively young donor kidneys with early severe TCMR, the prevalent disease phenotype. It was known that the interstitial inflammation and tubulitis lesions were non-specific and found in other conditions, for example, acute kidney injury (AKI) and progressive renal diseases, creating the potential for false positives [3-9], and difficult to assess in scarred tissue, creating false negatives . Many biopsies are assigned the ambiguous “borderline” designation: In our recent study of 403 biopsies, 35 biopsies were diagnosed TCMR but 40 were called borderline . Interobserver agreement is poor [12, 13], and certain diagnostic rules may be incorrect, for example the designation of all “isolated v-lesions” as TCMR [11, 14]. Central histology assessment is not the solution: It is simply a second opinion using the same standard. Indeed, in a study of independent central readings by three pathologists, when one diagnosed TCMR, another agreed in 45% of biopsies, and three agreed in only 16% .
Moreover, time has changed the biopsy case mix. Polyoma virus nephropathy (PVN), which had not been recognized in 1991, renders TCMR lesions of uncertain significance , and the 1991 criteria must now be interpreted in biopsies with more aging, injury and scarring. This “data drift”—a shift in disease prevalence and prior probabilities within the target population—is a common occurrence in epidemiological studies, and is to be expected in a diagnostic system in use for over two decades.
Gene expression in biopsies is emerging as a new diagnostic system for TCMR  and other diseases such as inflammatory bowel disease . While many investigators have analyzed molecular changes in biopsies with histologic rejection, using either RT-PCR [17, 18] or microarrays [19-21], these studies failed to distinguish TCMR from antibody-mediated rejection (ABMR), in part because the criteria for diagnosing ABMR, particularly C4d-negative ABMR, are evolving and remain controversial [22, 23]. Because of this, we established the Alberta Transplant Applied Genomics Centre (ATAGC) Reference Standard histology system (http://atagc.med.ualberta.ca/) in a prospective cohort of 403 indication biopsies (the BFC403 cohort). By setting out criteria for C4d-negative ABMR, the ATAGC Reference Standard classification permits the first rigorous separation of TCMR from ABMR [14, 24]. This allowed us to develop algorithms to assign TCMR scores that reflect the probability of TCMR in each biopsy. The TCMR scores correlated with the histologic TCMR lesions and diagnoses, and differentiated borderline biopsies into those with and without TCMR. The accuracy of the TCMR score for the histologic diagnosis was 89%, with most discrepancies between the TCMR score and histologic TCMR occurring in situations known to be challenging for histology, such as when the tissue is inflamed due to AKI, damaged by renal disease, or has extensive scarring. The TCMR score confirmed previous concerns [25, 26] about diagnosing TCMR on the basis of intimal arteritis (isolated v-lesions): 19/24 biopsies with isolated v-lesions had low negative TCMR scores. We concluded that many biopsies with low TCMR scores and histologic TCMR were histology false positives due to inflammation induced by AKI or renal diseases, and that some biopsies with high TCMR scores and histologic assessment indicating no TCMR were histology false negatives, that is, TCMR obscured by scarring or complicated by PVN, which can make pathologists reluctant to diagnose TCMR .
The present prospective multicenter INTERCOM study explored the potential impact of the TCMR score by comparing it to local diagnoses in six established programs in five countries. Using microarray measurements and an algorithm developed in the earlier BFC403 reference set , we assigned TCMR scores to 300 new indication biopsies (INT300). Our goals were to examine the histology-molecular relationships in INT300, and to compare them to the histology-molecular relationships in the BFC403 reference set. The hypothesis arising from the BFC403 reference set was that the TCMR score would predict histologic TCMR defined by these highly experienced local centers; that the relationship between the TCMR score and conventional assessment (e.g. accuracy) in INT300 would be similar to the reference set; and that the discrepancies would be concentrated in situations where histology was potentially misleading, as was the case in the reference set. Thus the INTERCOM study should provide an estimate of the potential error rate in histology assessment in experienced transplant centers.
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The demographics of the INT300 population are presented in Table S2, compared with the BFC403 reference set . The case mix in INT300 was similar to BFC403, as expected, since both are unselected indication biopsy populations. The median follow-up time of working grafts in the more recent INT300 (384 days) is shorter than in BFC403 (1135 days). The distribution of local diagnoses in INT300 was similar to the BFC403 reference set (Table 1). The category of “possible ABMR” used in BFC403 did not prove useful, and was eliminated in INTERCOM, with such biopsies assigned to other relevant categories.
Table 1. Histologic diagnoses
|Histology-DSA diagnosis||INTERCOM (INT300)1: interpreted from local biopsy reports||Reference set (BFC403): reference standard classification|
|Early (<1 year)||Late (>1 year)||All (% of total)||Early (<1 year)||Late (>1 year)||All (% of total)|
|TCMR||26||6||32 (11)||27||8||35 (9)|
|Mixed TCMR and ABMR||1||5||6 (2)||2||20||22 (5)|
|Borderline||24||22||46 (15)||29||13||42 (10)|
|ABMR (C4d+ and C4d−)||3||37||40 (13)||6||59||65 (16)|
|Transplant glomerulopathy||2||18||20 (7)||1||3||4 (1)|
|Polyoma virus nephropathy (PVN)||9||4||13 (4)||10||2||12 (3)|
|Glomerulonephritis||3||37||40 (40)||6||35||41 (10)|
|Acute kidney injury||14||0||14 (5)||50||0||50 (12)|
|No major abnormalities||24||19||43 (14)||34||42||76 (19)|
|Atrophy-fibrosis||8||30||38 (13)||6||34||40 (10)|
|Others||2||6||82 (3)||11||5||16 (4)|
|Total||116||184||300 (100)||182||221||403 (100)|
Initially, it was intended to include central review of the histology as an add-on but this proved to be impossible: Some centers were unwilling to submit their slides, and there was no way to resolve disagreements between the local center and the central reviewer.
By histology, 32 biopsies had TCMR and 6 had mixed rejection, that is, histologic criteria for both ABMR and TCMR. Biopsies with no rejection, borderline changes, or specific diseases were classified as AKI (n = 14) if they were before 42 days posttransplant. Those more than 42 days posttransplant were designated as either “no major abnormalities” (NOMOA) if they had minimal interstitial fibrosis (ci < 2, n = 43), or “atrophy-fibrosis of unknown significance” (IFTA) if there was scarring (ci > 1; n = 38). PVN (n = 13) was diagnosed by local standard of care (blood or urine polymerase chain reaction [PCR], immunohistochemistry, in situ hybridization and/or electron microscopy).
The majority of biopsies were late (>1 year posttransplant) in INT300 (61%), similar to the BFC403 reference set (55%). TCMR was diagnosed in 9% of BFC403 and 11% of INT300, mostly early; borderline in 10% of BFC403 and 15% of INT300 and mixed rejection in 5% of BFC403 biopsies and 2% of INT300 biopsies.
Relating TCMR scores to histologic lesions
TCMR scores in INT300 biopsies correlated with interstitial inflammation (r = 0.79), tubulitis (r = 0.83) and intimal arteritis (r = 0.85) (Table 2). The correlations in INT300 were similar to those in the BFC403 reference set . There was a weak correlation with peritubular capillaritis (r = 0.28), which sometimes occurs in TCMR as well as in ABMR . There were negative correlations with glomerular double contours, an ABMR lesion; with arterial fibrous intimal thickening and arteriolar hyalinosis; and with time posttransplant. The TCMR score was not associated with DSA positivity.
Table 2. Gamma rank correlations between TCMR scores1 and histologic lesions in the 300 INTERCOM biopsies and the reference set
|INTERCOM (INT300)||Reference set (previously published)|
|Interstitial inflammation (i)||0.79***||0.87***|
|TCMR and ABMR lesions|
|Intimal arteritis (v)||0.85*||0.76**|
|Peritubular capillaritis (ptc)||0.28||0.36*|
|Glomerular double contours (cg)||−0.58***||−0.62***|
|Interstitial fibrosis (ci)||−0.05||−0.04|
|Tubular atrophy (ct)||−0.06||0.11|
|Arterial fibrous intimal thickening (cv)||−0.29*||−0.20|
|Arteriolar hyalinosis (ah)||−0.65***||−0.55***|
|Time posttransplant: early (<1 year) vs. late (>1 year)||−0.31*||−0.51**|
Agreement between the TCMR score and histology diagnosis
TCMR scores were assigned by the classifier generated in the BFC403 reference set (Figure 1). For comparison with local histology assessments, TCMR scores were divided into high or low using the same cutoff of 0.1 as in the BFC403 reference set.
Figure 1. Relationship between the TCMR score and the diagnoses based on local center assessment in INT300. The order within each diagnostic category is random. The horizontal line shows our threshold of 0.1 for defining high versus low TCMR scores. The different symbols represent time posttransplantation: early (<1 year: empty triangles) and late (>1 year: solid triangles). ABMR, antibody-mediated rejection; TG, transplant glomerulopathy; M, mixed; TCMR, T cell-mediated rejection; Bord., borderline; PVN, polyoma virus nephropathy; GN, glomerulonephritis; AKI, acute kidney injury; NOMOA, no major abnormalities; Oth., others; Neph., nephrectomies.
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We studied the relationship between TCMR scores and histologic diagnoses of TCMR in INT300, and compared this to the histology-molecular relationship in the BFC403 reference set (Figure 2). The area under the receiver operating characteristic curve (AUC) of the TCMR score for a primary histologic diagnosis of TCMR or mixed was 0.85, similar to that in BFC403, with borderline considered not TCMR. When borderline biopsies were excluded, the AUCs rose to 0.86 in INT300 and 0.85 in BFC403 (Table 3). Thus the relationship between the TCMR and the conventional assessment in INT300 was virtually identical to that in BFC403.
Figure 2. Prediction of TCMR and mixed histologic diagnosis by TCMR score. Receiver operating characteristic (ROC) curve and area under curve (AUC) for the ability of the TCMR score to predict the local primary diagnosis of TCMR or mixed rejection in INT300 (n = 300) and in BFC403 (n = 403).
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Table 3. Agreement of the TCMR score with histologic diagnosis
|TCMR score||Primary histologic diagnosis||Total|
|TCMR||Detailed diagnosis in non-TCMR biopsies|
|TCMR||Mixed||TCMR and mixed||ABMR (C4d+ and C4d−)||TG||PVN||GN||Acute kidney injury||No major abnormalities||Atrophy fibrosis||Other||All non-TCMR except borderline||Borderline|
| ||Prediction of the local diagnosis of TCMR or mixed rejection by the TCMR score|
|TCMR score in all biopsies|
|TCMR score after leaving out borderline biopsies|
High TCMR scores in INT300 biopsies were mostly in biopsies with histology diagnoses of TCMR or borderline: of 45 biopsies with high TCMR scores, 22 had histology diagnoses of TCMR (n = 21) or mixed (n = 1) and 8 were classified as borderline by histology (Table 3). Most biopsies with histologic TCMR or mixed rejection had elevated TCMR scores (22/38), whereas biopsies with IFTA or AKI or other histologic diagnoses (e.g. ABMR) did not. Of 46 histologic borderline biopsies, 38 were TCMR score negative, confirming previous conclusions .
In summary, compared to histology, the TCMR score reclassified 77/300 biopsies (26%): 15 histologic TCMR negative biopsies were TCMR score positive; 16 biopsies with histologic TCMR (including five mixed) were TCMR score negative and the 46 borderline biopsies were reclassified as TCMR score positive (n = 8) or negative (n = 38).
Discrepancies between the TCMR score and the primary diagnosis were of two types: score positive/histology negative and score negative/histology positive (Figure 3). The distribution of the two classes of discrepancies over time was different, both from each other and from that of the biopsies where both systems agreed TCMR was present.
Figure 3. Smoothed frequency distribution (conditional probability) for TCMR/mixed over time. Score − Histology+: TCMR score negative and TCMR or mixed histologic diagnosis positive; Score + Histology+: TCMR score positive and TCMR or mixed histologic diagnosis positive; Score + Histology−: TCMR score positive and TCMR or mixed histologic diagnosis negative. Comparison of Score − Histology+ and Score + Histology− over time: Chi-square p-value <0.05.
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The 15 score positive/histology negative biopsies were predominantly late—12 were beyond 1 year—and none was earlier than 6 weeks posttransplant (Figure 3). However, despite having other primary diagnoses, 9 of the 15 (Table 4) had secondary diagnoses or text descriptions of lesions meeting the criteria for TCMR or borderline changes, and thus represent under-reporting of TCMR-like changes, not discrepancies (including six with PVN as discussed below). Of the remaining six, three were late with severe scarring (ci3); one had a TCMR score of 0.11 and may represent a false positive TCMR score at the cutoff used (0.10) and two were unexplained.
Table 4. Score positive–histology negative: TCMR score positive biopsies with primary histology diagnosis other than TCMR or mixed or borderline (n = 15)1
|Biopsy identification||Histologic diagnosis||GFR||Borderline secondary diagnosis present||TCMR secondary diagnosis present||Borderline lesions present2||TCMR lesions present3||Fibrosis ci > 1||Proposed interpretation, based on categories proposed in reference set|
|48||C4d + ABMR||—|| || || || ||X||TCMR obscured by scarring (ci = 3)|
|220||C4d + ABMR||42|| || || || || ||Unexplained|
|58||C4d − ABMR||42||X|| ||X|| ||X||Secondary diagnosis of “borderline”|
|103||C4d − ABMR||52|| || || ||X|| ||Meets criteria for “TCMR”|
|178||C4d − ABMR||23||X|| ||X|| ||X||Secondary diagnosis of “borderline”|
|5||GN||31||X|| ||X|| || ||Secondary diagnosis of “borderline”|
|52||IFTA||—|| || || || ||X||TCMR obscured by scarring (ci = 3)|
|168||IFTA||43|| || || || ||X||TCMR obscured by scarring (ci = 3)|
|7||NOMOA||31|| || || || || ||Unexplained|
|113||PVN||19|| || || ||X||X||Meets criteria for “TCMR”|
|32||PVN||31|| ||X|| ||X|| ||Meets criteria for “TCMR”|
|205||PVN||18|| || || ||X|| ||Meets criteria for “TCMR”|
|249||PVN||—|| || || ||i3, tNA|| ||Meets i-score criteria for “TCMR”|
|67||PVN||<20|| || || ||X||X||Meets criteria for “TCMR”|
|217||PVN||25|| || ||X|| || ||Meets criteria for “borderline”|
Most of the 16 score negative/histology positive biopsies were either very early (six <42 days) or late (six >1 year) (Table 5; Figure 3). Three of the six early biopsies may reflect interstitial inflammation and tubulitis induced by AKI, as described in protocol biopsies , and three were diagnosed exclusively on the basis of the questionable isolated v-lesion criterion, raising the possibility that endothelial injury at the time of transplantation can produce isolated v-lesions. It is possible that at least some of the other nine reflect tubulitis and interstitial inflammation due to parenchymal damage induced by diseases such as ABMR, since these features are common in primary renal diseases [6-9]. One was pretreated before the biopsy, which may eliminate molecular changes despite persistent histologic lesions .
Table 5. Score negative–histology positive: histology diagnosis of TCMR with TCMR score <0.1 (n = 16)
|Biopsy ID||Histologic diagnosis||Time from transplant||GFR||Treatment||Indication||Isolated v-lesions present1||TCMR lesions present2||Suggested interpretation, based on categories proposed in reference set|
|42||TCMR||16||46||No||DRF||X|| ||Histology false positive due to isolated v-lesions|
|158||TCMR||10||60||NA||SIRF|| ||X||Histology false positive: inflammation due to AKI|
|256||TCMR||247||40||No||DRF|| ||X||Histology false positive: persistent parenchymal damage in old donor kidney (donor age 75)|
|302||TCMR||33||43||No||DRF|| ||X||Histology false positive: inflammation due to AKI|
|76||TCMR||160||27||No||DRF|| ||X||Histology false positive: persistent parenchymal damage in old donor kidney (donor age 72)|
|125||TCMR||209||87||No||Investigate proteinuria|| ||X||Unexplained|
|240||TCMR||101||103||No||DRF|| ||X||Unexplained; history of DGF|
|151||TCMR||14||NA||Yes||DGF||X|| ||Histology false positive due to isolated v-lesions; pretreated|
|81||TCMR||5||17||No||DGF|| ||X||Histology false positive: inflammation due to AKI|
|137||Mixed||3242||62||No||Investigate proteinuria|| ||X||Histology false positive: inflammation/tubulitis due to severe parenchymal damage from ABMR|
|127||Mixed||2821||NA||No||DRF|| ||X||Histology false positive: inflammation/tubulitis due to severe parenchymal damage from ABMR|
|282||Mixed||2482||41||NA||DRF|| ||X||Histology false positive: inflammation/tubulitis due to severe parenchymal damage from ABMR|
|294||Mixed||26||28||Yes||DRF||X|| ||Histology false positive due to isolated v-lesions; pretreated|
Thus the discrepancies confirmed the predictions of the BFC403 study that the disagreement between the TCMR score and histology is not random but is concentrated in situations where histology has known potential for false negatives and false positives.
The number of times the pathologists indicated biopsy inadequacies with respect to TCMR features was as follows: 8 of the 300 lacked t, 7 lacked i and 21 lacked v. These included some biopsies with multiple missing lesions, and there were 25 biopsies in total with missing i/t/v data. Of these 25 biopsies (all called non-TCMR by histology), only 1 had a TCMR score >0.1, so their contribution to the number of discrepancies was minimal.
Polyoma virus nephropathy
The six biopsies diagnosed locally as PVN with high TCMR scores were between 4 and 12 months (Table 6) and all had histologic criteria for TCMR or borderline. One actually had a secondary histologic diagnosis of TCMR, and five had TCMR lesions recorded but not reported as TCMR, reflecting the uncertainty over the significance of these lesions in PVN . Two other PVN biopsies with inflammation (i- or t-lesions) had very low TCMR scores, confirming the suspicion that injury from PVN can induce lesions that mimic TCMR .
Table 6. TCMR scores and TCMR histologic lesions in biopsies diagnosed as PVN nephropathy in INT300
|Biopsy identification||Secondary diagnosis||TCMR score||i||t||v||g||ptc||ci||ti||ct||cv||cg||ah||mm|
Relationship of TCMR to graft survival
Patients with TCMR (TCMR score or histology) in any biopsy were compared to all other patients. Survival in the TCMR group was calculated as the time after the first biopsy showing TCMR. There was no association of the TCMR score or histologic TCMR with graft survival in either INT300 or BFC403 (Table 7).
Table 7. Univariate analysis of the effect of TCMR on death-censored graft survival using a Cox proportional hazards regression model
|Variable||Failures as a fraction of total patients with TCMR1||Hazard ratio for TCMR in the whole population (95% confidence interval)||P-value|
|INT300 (n = 264 patients with 33 failures)|
|Positive TCMR score||8/43||1.79 (0.80–4)||0.15|
|Histologic diagnosis of TCMR or mixed||3/35||0.57 (0.17–1.90)||0.36|
|BFC403 (n = 315 patients with 80 failures)|
|Positive TCMR score||11/42||1.1 (0.58–2.07)||0.76|
|Histologic diagnosis of TCMR or mixed||11/46||0.83 (0.44–1.58)||0.58|
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To assess the potential clinical impact of molecular diagnosis of TCMR, this prospective multicenter study generated microarray-based TCMR scores in a population of 300 recent indication biopsies of kidney transplants (INT300), using an algorithm developed in the BFC403 reference set. We assessed the ability of the TCMR score to predict local histologic standard-of-care diagnoses, established the relationship between the molecular and histologic variables in INT300, and compared these findings to the BFC403 reference set. The case mix in INT300 was similar to that in the reference set. The scores from the previously generated TCMR classifier correlated with histologic TCMR lesions and TCMR diagnoses by the local centers, in a pattern similar to the reference set in terms of accuracy and AUC, with discrepancies in the same situations. The TCMR score changed the assessment of 26% of biopsies, particularly in situations known to be challenging for histology such as PVN, scarring, and when the tissue had AKI or had been damaged by renal diseases, as predicted by the BFC403 study. Neither the TCMR score nor histologic TCMR was associated with increased risk of failure in INT300, consistent with the findings in BFC403. The results establish the feasibility and utility of central microarray-based molecular measurements to estimate disease states in transplant biopsies, and support the potential of molecular testing of biopsies used in conjunction with histology to increase our understanding of these diseases.
As the first multicenter study of a previously developed biopsy diagnostic system in transplantation, INTERCOM had to invent a design to compare the TCMR score to the actual diagnoses applied in leading transplant centers. We followed the lead of the ISD trials, in which the primary endpoint was local assessment. We did not prescribe what experienced centers “should” be doing because there was no evidence to indicate what choices would be more correct than local opinion in experienced centers. Histology diagnosis is an expert opinion-based discipline, in which the guidelines of the self-organizing Banff group and other groups are useful but their application is voluntary, and the actual degree of application in experienced transplant centers is not known. For example, we had previously discovered that some pathologists who nominally subscribe to the Banff published guidelines actually use personal variants that significantly affect the diagnosis of TCMR .
The TCMR score offers an opportunity to improve our understanding of PVN, where histology struggles to distinguish TCMR from immune reactions to viral products and inflammatory reactions to virus-induced epithelial injury. While one can speculate that the T cell response to virus proteins could give positive TCMR scores, this is in our opinion unlikely, since in most cases of PVN in the INT300 and in BFC403 the TCMR score was negative, even in some cases where the biopsy was inflamed. Our results argue that PVN biopsies with high TCMR scores actually have TCMR, perhaps triggered by ISD reduction. This is supported by our finding that some biopsies in both BFC403 and INT300 were inflamed but did not have positive TCMR scores. The presence of TCMR lesions but no TCMR diagnosis in some PNV biopsies may reflect reluctance of the pathologists to diagnose TCMR when other diseases are present, given the known nonspecificity of these lesions in AKI and in primary kidney diseases. Resolution of this issue must await further prospective trials, and ultimately criteria that truly distinguish allorecognition from immune recognition of viral products.
Although TCMR had no impact on subsequent graft survival, whether diagnosed by the TCMR score or by histology, further study is needed to define the outcomes for kidneys with TCMR score positive biopsies currently being called negative by histology. Late “rejection” is a known risk for graft loss , but these studies had no way of distinguishing TCMR from ABMR reliably. Late ABMR carries a very poor prognosis , but late TCMR may also be significant, for example as a signal that the immunosuppressive regimen is inadequate, perhaps reflecting nonadherence  or “minimization” of ISD doses. The present findings raise the possibility that the TCMR component of these complex late biopsies may often be missed by histology, and the issue must now be revisited.
While it is not possible to explain the discrepancies with certainty, the present results confirm the earlier conclusion that discrepancies are not random but are in situations where histology is known to be limited, including those in which TCMR lesions are difficult to assess (severe scarring) or when TCMR lesions may be induced by other diseases. The relationship of histology diagnosis to the TCMR score (AUC) was similar to that in the BFC403 reference set. As predicted , score positive–histology negative biopsies often occurred in PVN and in late biopsies with scarring. Score negative–histology positive biopsies often occurred in kidneys that had other potential causes of parenchymal damage, including early AKI, persistent parenchymal dysfunction in old donor kidneys, or active nephron damage in ABMR. Since tubulitis and interstitial inflammation are well known in primary renal diseases, their presence in some kidney transplants due to processes other than TCMR is expected. This would explain why the finding of these lesions in protocol biopsies fails to predict progression  and or benefit from treatment .
The INTERCOM study provides an estimate of the potential error rate in histologic diagnosis of TCMR in leadership centers using the international standard-of-care, and offers a solution to reducing these errors through new prospective studies focusing on the scenarios where the discrepancies occurred. The TCMR score offers new insight in biopsies called “borderline”, an ambiguous category that is a weakness of the consensus guidelines  and has the potential to cause both over- and under-treatment. The TCMR score will also be welcome in kidneys with scarring that can obscure TCMR lesions. Moreover, in AKI and in progressive parenchymal diseases such as ABMR or GN, where nephron damage can induce tubulitis and interstitial inflammation that mimics TCMR lesions, the TCMR score can potentially identify which cases actually need treatment of TCMR.
With microarray assessment of biopsies, renal transplantation joins other areas of medicine moving toward precision diagnostic systems as standard of care for serious illnesses , and is a step in the direction of evidence based practice, moving from expert opinion to formal prospective studies (for levels of evidence see the US Preventive Services Task Force [http://www.uspreventiveservicestaskforce.org/]). Molecular assessment of cancer tissue is proving useful in tests such as Oncotype DX [36, 37] and Mammaprint [38, 39], but application of molecular testing to noncancer biopsies with inflammatory diseases is only beginning. For example, we are proceeding to assess the TCMR score in other organ transplants such as heart and lung, where histology assessment is even more problematic than in kidney transplants, and in nontransplant biopsies diseases such inflammatory bowel disease . The TCMR score was reliable in operation, providing answers in all 300 biopsies that had been stabilized properly for analysis, with a turnaround of 30 h after receiving the sample. Moreover, this “first generation” TCMR score has considerable potential for refinement, for example, by incorporating other analytical approaches such as ensemble methods .
The low sensitivity and positive predictive value of the TCMR score for a histology diagnosis of TCMR suggest that we temper our interpretations until further experience is available. However, these values are similar to the sensitivity and positive predictive values (average 0.45) between expert pathologists . This reflects the problem of “reference standard-related bias”: a new test, whether based on a molecular classifier or the opinion of a second pathologist, will not agree perfectly with the existing gold standard (the opinion of a different pathologist) when there is a high degree of subjectivity involved in the assignment of the gold standard. “Noise” within histology is intrinsic, due to the poor kappa values for TCMR lesions, for example, tubulitis 0.17, where complete agreement is 1.0 and random is 0.0 [12, 13]. For the present, the clearest value of the TCMR score will be in problematic cases, and its limited ability to provide information about diagnoses such as recurrent diseases must be acknowledged.
While the present study was designed to compare the TCMR score to the standard of care without distorting what the best centers are actually doing, the study could have been improved by imposing a supplementary research checklist for reporting, which we now recommend for future studies. Our design was to reflect actual reporting, following the principle of “intention-to-treat” analysis established in multicenter drug trials. We hope that our design offers lessons for future studies in which new tests are compared to classic practices, but always ensuring that such additions do not require deviation from the standard of care in these centers.