Michael G. Walker

Discovery and validation of cell cycle arrest biomarkers in human acute kidney injury

IntroductionAcute kidney injury (AKI) can evolve quickly and clinical measures of function often fail to detect AKI at a time when interventions are likely to provide benefit. Identifying early markers of kidney damage has been difficult due to the complex nature of human AKI, in which multiple etiologies exist. The objective of this study was to identify and validate novel biomarkers of AKI.MethodsWe performed two multicenter observational studies in critically ill patients at risk for AKI - discovery and validation. The top two markers from discovery were validated in a second study (Sapphire) and compared to a number of previously described biomarkers. In the discovery phase, we enrolled 522 adults in three distinct cohorts including patients with sepsis, shock, major surgery, and trauma and examined over 300 markers. In the Sapphire validation study, we enrolled 744 adult subjects with critical illness and without evidence of AKI at enrollment; the final analysis cohort was a heterogeneous sample of 728 critically ill patients. The primary endpoint was moderate to severe AKI (KDIGO stage 2 to 3) within 12 hours of sample collection.ResultsModerate to severe AKI occurred in 14% of Sapphire subjects. The two top biomarkers from discovery were validated. Urine insulin-like growth factor-binding protein 7 (IGFBP7) and tissue inhibitor of metalloproteinases-2 (TIMP-2), both inducers of G1 cell cycle arrest, a key mechanism implicated in AKI, together demonstrated an AUC of 0.80 (0.76 and 0.79 alone). Urine [TIMP-2]·[IGFBP7] was significantly superior to all previously described markers of AKI (P <0.002), none of which achieved an AUC >0.72. Furthermore, [TIMP-2]·[IGFBP7] significantly improved risk stratification when added to a nine-variable clinical model when analyzed using Cox proportional hazards model, generalized estimating equation, integrated discrimination improvement or net reclassification improvement. Finally, in sensitivity analyses [TIMP-2]·[IGFBP7] remained significant and superior to all other markers regardless of changes in reference creatinine method.ConclusionsTwo novel markers for AKI have been identified and validated in independent multicenter cohorts. Both markers are superior to existing markers, provide additional information over clinical variables and add mechanistic insight into AKI.Trial registrationClinicalTrials.gov number NCT01209169.

Validation of Cell-Cycle Arrest Biomarkers for Acute Kidney Injury Using Clinical Adjudication

RATIONALE We recently reported two novel biomarkers for acute kidney injury (AKI), tissue inhibitor of metalloproteinases (TIMP)-2 and insulin-like growth factor binding protein 7 (IGFBP7), both related to G1 cell cycle arrest. OBJECTIVES We now validate a clinical test for urinary [TIMP-2]·[IGFBP7] at a high-sensitivity cutoff greater than 0.3 for AKI risk stratification in a diverse population of critically ill patients. METHODS We conducted a prospective multicenter study of 420 critically ill patients. The primary analysis was the ability of urinary [TIMP-2]·[IGFBP7] to predict moderate to severe AKI within 12 hours. AKI was adjudicated by a committee of three independent expert nephrologists who were masked to the results of the test. MEASUREMENTS AND MAIN RESULTS Urinary TIMP-2 and IGFBP7 were measured using a clinical immunoassay platform. The primary endpoint was reached in 17% of patients. For a single urinary [TIMP-2]·[IGFBP7] test, sensitivity at the prespecified high-sensitivity cutoff of 0.3 (ng/ml)(2)/1,000 was 92% (95% confidence interval [CI], 85-98%) with a negative likelihood ratio of 0.18 (95% CI, 0.06-0.33). Critically ill patients with urinary [TIMP-2]·[IGFBP7] greater than 0.3 had seven times the risk for AKI (95% CI, 4-22) compared with critically ill patients with a test result below 0.3. In a multivariate model including clinical information, urinary [TIMP-2]·[IGFBP7] remained statistically significant and a strong predictor of AKI (area under the curve, 0.70, 95% CI, 0.63-0.76 for clinical variables alone, vs. area under the curve, 0.86, 95% CI, 0.80-0.90 for clinical variables plus [TIMP-2]·[IGFBP7]). CONCLUSIONS Urinary [TIMP-2]·[IGFBP7] greater than 0.3 (ng/ml)(2)/1,000 identifies patients at risk for imminent AKI. Clinical trial registered with www.clinicaltrials.gov (NCT 01573962).

Derivation and validation of cutoffs for clinical use of cell cycle arrest biomarkers

Background Acute kidney injury (AKI) remains a deadly condition. Tissue inhibitor of metalloproteinases (TIMP)-2 and insulin-like growth factor binding protein (IGFBP)7 are two recently discovered urinary biomarkers for AKI. We now report on the development, and diagnostic accuracy of two clinical cutoffs for a test using these markers. Methods We derived cutoffs based on sensitivity and specificity for prediction of Kidney Disease: Improving Global Outcomes Stages 2–3 AKI within 12 h using data from a previously published multicenter cohort (Sapphire). Next, we verified these cutoffs in a new study (Opal) enrolling 154 critically ill adults from six sites in the USA. Results One hundred subjects (14%) in Sapphire and 27 (18%) in Opal met the primary end point. The results of the Opal study replicated those of Sapphire. Relative risk (95% CI) in both studies for subjects testing at ≤0.3 versus >0.3–2 were 4.7 (1.5–16) and 4.4 (2.5–8.7), or 12 (4.2–40) and 18 (10–37) for ≤0.3 versus >2. For the 0.3 cutoff, sensitivity was 89% in both studies, and specificity 50 and 53%. For 2.0, sensitivity was 42 and 44%, and specificity 95 and 90%. Conclusions Urinary [TIMP-2]•[IGFBP7] values of 0.3 or greater identify patients at high risk and those >2 at highest risk for AKI and provide new information to support clinical decision-making. Clinical Trials Registration Clintrials.gov # NCT01209169 (Sapphire) and NCT01846884 (Opal).

Drug Target Discovery by Gene Expression Analysis Cell Cycle Genes

Gene expression microarrays and gene expression databases provide new opportunities for the discovery of drug targets and for determination of a drugs mode of action. We review gene expression analysis methods and describe studies that have identified cell cycle genes using differential expression analysis and co-expression analysis. We present an example of the identification of previously-unrecognized human cell cycle genes, CDCA1 through CDCA8, that are co-expressed with known cell cycle genes including CDC2, CDC7, CDC23, cyclin, MCAK, mki67a, topoisomerase II, and others.

SynDB: a Synapse protein DataBase based on synapse ontology

A synapse is the junction across which a nerve impulse passes from an axon terminal to a neuron, muscle cell or gland cell. The functions and building molecules of the synapse are essential to almost all neurobiological processes. To describe synaptic structures and functions, we have developed Synapse Ontology (SynO), a hierarchical representation that includes 177 terms with hundreds of synonyms and branches up to eight levels deep. associated 125 additional protein keywords and 109 InterPro domains with these SynO terms. Using a combination of automated keyword searches, domain searches and manual curation, we collected 14 000 non-redundant synapse-related proteins, including 3000 in human. We extensively annotated the proteins with information about sequence, structure, function, expression, pathways, interactions and disease associations and with hyperlinks to external databases. The data are stored and presented in the Synapse protein DataBase (SynDB, ). SynDB can be interactively browsed by SynO, Gene Ontology (GO), domain families, species, chromosomal locations or Tribe-MCL clusters. It can also be searched by text (including Boolean operators) or by sequence similarity. SynDB is the most comprehensive database to date for synaptic proteins.

A multicenter study directly comparing the diagnostic accuracy of gene expression profiling and immunohistochemistry for primary site identification in metastatic tumors.

Metastatic tumors with an uncertain primary site can be a difficult clinical problem. In tens of thousands of patients every year, no confident diagnosis is ever issued, making standard-of-care treatment impossible. Gene expression profiling (GEP) tests currently available to analyze these difficult-to-diagnose tumors have never been directly compared with the diagnostic standard of care, immunochemistry (IHC). This prospectively conducted, blinded, multicenter study compares the diagnostic accuracy of GEP with IHC in identifying the primary site of 157 formalin-fixed paraffin-embedded specimens from metastatic tumors with known primaries, representing the 15 tissues on the GEP test panel. Four pathologists rendered diagnoses by selecting from 84 stains in 2 rounds. GEP was performed using the Pathwork Tissue of Origin Test. Overall, GEP accurately identified 89% of specimens, compared with 83% accuracy using IHC (P=0.013). In the subset of 33 poorly differentiated and undifferentiated carcinomas, GEP accuracy exceeded that of IHC (91% to 71%, P=0.023). In specimens for which pathologists rendered their final diagnosis with a single round of stains, both IHC and GEP exceeded 90% accuracy. However, when the diagnosis required a second round, IHC significantly underperformed GEP (67% to 83%, P<0.001). GEP has been validated as accurate in diagnosing the primary site in metastatic tumors. The Pathwork Tissue of Origin Test used in this study was significantly more accurate than IHC when used to identify the primary site, with the most pronounced superiority observed in specimens that required a second round of stains and in poorly differentiated and undifferentiated metastatic carcinomas.

Clinical Implications and Longitudinal Alteration of Peripheral Blood Transcriptional Signals Indicative of Future Cardiac Allograft Rejection

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 =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.

Z39Ig is co-expressed with activated macrophage genes

Z39Ig is a recently-identified gene with immunoglobulin-like domains whose function is unknown. We examined expression of Z39Ig in 1432 human cDNA libraries, and found it primarily in synovium of patients with rheumatoid arthritis, in placenta, and in lung. We analyzed its co-expression pattern using the Guilt-by-Association (GBA) algorithm, and found that it is most similar in expression to early genes in the classical complement system (C1qA, C1qB, C1qC, C1r, and C1 inhibitor), MHC class II genes (HLA-DR alpha, HLA-DR beta 1, and HLA-DP alpha 1), Fc receptors (Fc gamma RIIa and Fc epsilon R1), lysosomal protein (LAPTm5), tissue transglutaminase, and macrophage receptors (MARCO and CD163/M130). The sequence and expression data suggest that Z39Ig is a cell surface receptor, expressed in activated macrophages, and linked with the classical complement system, most likely in phagocytosis preceding antigen presentation. Knowledge of this gene may contribute to better understanding of the role of complement and activated macrophages in rheumatoid arthritis and systemic lupus.

Validation of a Multiprotein Plasma Classifier to Identify Benign Lung Nodules

Introduction: Indeterminate pulmonary nodules (IPNs) lack clinical or radiographic features of benign etiologies and often undergo invasive procedures unnecessarily, suggesting potential roles for diagnostic adjuncts using molecular biomarkers. The primary objective was to validate a multivariate classifier that identifies likely benign lung nodules by assaying plasma protein expression levels, yielding a range of probability estimates based on high negative predictive values (NPVs) for patients with 8 to 30 mm IPNs. Methods: A retrospective, multicenter, case-control study was performed using multiple reaction monitoring mass spectrometry, a classifier comprising five diagnostic and six normalization proteins, and blinded analysis of an independent validation set of plasma samples. Results: The classifier achieved validation on 141 lung nodule-associated plasma samples based on predefined statistical goals to optimize sensitivity. Using a population based nonsmall-cell lung cancer prevalence estimate of 23% for 8 to 30 mm IPNs, the classifier identified likely benign lung nodules with 90% negative predictive value and 26% positive predictive value, as shown in our prior work, at 92% sensitivity and 20% specificity, with the lower bound of the classifier’s performance at 70% sensitivity and 48% specificity. Classifier scores for the overall cohort were statistically independent of patient age, tobacco use, nodule size, and chronic obstructive pulmonary disease diagnosis. The classifier also demonstrated incremental diagnostic performance in combination with a four-parameter clinical model. Conclusions: This proteomic classifier provides a range of probability estimates for the likelihood of a benign etiology that may serve as a noninvasive, diagnostic adjunct for clinical assessments of patients with IPNs.

Pharmaceutical target identification by gene expression analysis.

The majority of newly-identified genes in the human genome show no significant sequence similarity to genes whose function is known, so they are not easily recognized as potential drug targets. Expression analysis is an alternative method to suggest possible functions of genes. We review statistical methods for gene expression analysis to identify potential pharmaceutical targets. Specifically, we illustrate the analysis of differential gene expression (using discriminant analysis, t-tests, and analysis of variance) and co-expression (using correlation, clustering, and chi-square). We present an example of the use of expression analysis to identify co-expressed cardiomyopathy-associated genes.