Workload Measurement in Subspecialty Placental Pathology in Canada.

BACKGROUND: Workload measurement is important to help determine optimal staffing and workload distribution for pathology laboratories. The Level 4 Equivalent (L4E) System is the most widely used Anatomical Pathology (AP) workload measurement tool in Canada. However, it was initially not developed with subspecialties in mind. METHODS: In 2016, a Pan-Canadian Pediatric-Perinatal Pathology Workload Committee (PCPPPWC) was organized to adapt the L4E System to assess Pediatric-Perinatal Pathology workload. Four working groups were formed. The Placental Pathology Working Group was tasked to develop a scheme for fair valuation of placental specimens signed out by subspecialists in the context of the L4E System. Previous experience, informal time and motion studies, a survey of Canadian Pediatric-Perinatal Pathologists, and interviews of Pathologists' Assistants (PA) informed the development of such scheme. RESULTS: A workload measurement scheme with average L4E workload values for examination and reporting of singleton and multiple gestation placentas was proposed. The proposal was approved by the Canadian Association of Pathologist - Association canadienne des pathologistes Workload and Human Resources Committee for adoption into the L4E System. CONCLUSION: The development of a workload measurement model for placental specimens provides an average and fair valuation of these specimen types, enabling its use for resource planning and workload distribution.

Integrating untargeted metabolomics, genetically informed causal inference, and pathway enrichment to define the obesity metabolome.

BACKGROUND: Obesity and its associated diseases are major health problems characterized by extensive metabolic disturbances. Understanding the causal connections between these phenotypes and variation in metabolite levels can uncover relevant biology and inform novel intervention strategies. Recent studies have combined metabolite profiling with genetic instrumental variable (IV) analysis (Mendelian randomization) to infer the direction of causality between metabolites and obesity, but often omitted a large portion of untargeted profiling data consisting of unknown, unidentified metabolite signals. METHODS: We expanded upon previous research by identifying body mass index (BMI)-associated metabolites in multiple untargeted metabolomics datasets, and then performing bidirectional IV analysis to classify metabolites based on their inferred causal relationships with BMI. Meta-analysis and pathway analysis of both known and unknown metabolites across datasets were enabled by our recently developed bioinformatics suite, PAIRUP-MS. RESULTS: We identified ten known metabolites that are more likely to be causes (e.g., alpha-hydroxybutyrate) or effects (e.g., valine) of BMI, or may have more complex bidirectional cause-effect relationships with BMI (e.g., glycine). Importantly, we also identified about five times more unknown than known metabolites in each of these three categories. Pathway analysis incorporating both known and unknown metabolites prioritized 40 enriched (p < 0.05) metabolite sets for the cause versus effect groups, providing further support that these two metabolite groups are linked to obesity via distinct biological mechanisms. CONCLUSIONS: These findings demonstrate the potential utility of our approach to uncover causal connections with obesity from untargeted metabolomics datasets. Combining genetically informed causal inference with the ability to map unknown metabolites across datasets provides a path to jointly analyze many untargeted datasets with obesity or other phenotypes. This approach, applied to larger datasets with genotype and untargeted metabolite data, should generate sufficient power for robust discovery and replication of causal biological connections between metabolites and various human diseases.

Homozygous GLUL deletion is embryonically viable and leads to glutamine synthetase deficiency.

Glutamine synthetase (GS) is the enzyme responsible for the biosynthesis of glutamine, providing the only source of endogenous glutamine necessary for several critical metabolic and developmental pathways. GS deficiency, caused by pathogenic variants in the glutamate-ammonia ligase (GLUL) gene, is a rare autosomal recessive inborn error of metabolism characterized by systemic glutamine deficiency, persistent moderate hyperammonemia, and clinically devastating seizures and multi-organ failure shortly after birth. The four cases reported thus far were caused by homozygous GLUL missense variants. We report a case of GS deficiency caused by homozygous GLUL gene deletion, diagnosed prenatally and likely representing the most severe end of the spectrum. We expand the known phenotype of this rare condition with novel dysmorphic, radiographic and neuropathologic features identified on post-mortem examination. The biallelic deletion identified in this case also included the RNASEL gene and was associated with immune dysfunction in the fetus. This case demonstrates that total absence of the GLUL gene in humans is viable beyond the embryonic period, despite the early embryonic lethality found in GLUL animal models.

PAIRUP-MS: Pathway analysis and imputation to relate unknowns in profiles from mass spectrometry-based metabolite data.

Metabolomics is a powerful approach for discovering biomarkers and for characterizing the biochemical consequences of genetic variation. While untargeted metabolite profiling can measure thousands of signals in a single experiment, many biologically meaningful signals cannot be readily identified as known metabolites nor compared across datasets, making it difficult to infer biology and to conduct well-powered meta-analyses across studies. To overcome these challenges, we developed a suite of computational methods, PAIRUP-MS, to match metabolite signals across mass spectrometry-based profiling datasets and to generate metabolic pathway annotations for these signals. To pair up signals measured in different datasets, where retention times (RT) are often not comparable or even available, we implemented an imputation-based approach that only requires mass-to-charge ratios (m/z). As validation, we treated each shared known metabolite as an unmatched signal and showed that PAIRUP-MS correctly matched 70-88% of these metabolites from among thousands of signals, equaling or outperforming a standard m/z- and RT-based approach. We performed further validation using genetic data: the most stringent set of matched signals and shared knowns showed comparable consistency of genetic associations across datasets. Next, we developed a pathway reconstitution method to annotate unknown signals using curated metabolic pathways containing known metabolites. We performed genetic validation for the generated annotations, showing that annotated signals associated with gene variants were more likely to be enriched for pathways functionally related to the genes compared to random expectation. Finally, we applied PAIRUP-MS to study associations between metabolites and genetic variants or body mass index (BMI) across multiple datasets, identifying up to ~6 times more significant signals and many more BMI-associated pathways compared to the standard practice of only analyzing known metabolites. These results demonstrate that PAIRUP-MS enables analysis of unknown signals in a robust, biologically meaningful manner and provides a path to more comprehensive, well-powered studies of untargeted metabolomics data.

Proteomic basis of mortality resilience mediated by FOXO3 longevity genotype.

FOXO3 is a ubiquitous transcription factor expressed in response to cellular stress caused by nutrient deprivation, inflammatory cytokines, reactive oxygen species, radiation, hypoxia, and other factors. We showed previously that the association of inherited FOXO3 variants with longevity was the result of partial protection against mortality risk posed by aging-related life-long stressors, particularly cardiometabolic disease. We then referred to the longevity-associated genotypes as conferring "mortality resilience." Serum proteins whose levels change with aging and are associated with mortality risk may be considered as "stress proteins." They may serve as indirect measures of life-long stress. Our aims were to (1) identify stress proteins that increase with aging and are associated with an increased risk of mortality, and (2) to determine if FOXO3 longevity/resilience genotype dampens the expected increase in mortality risk they pose. A total of 4500 serum protein aptamers were quantified using the Somalogic SomaScan proteomics platform in the current study of 975 men aged 71-83 years. Stress proteins associated with mortality were identified. We then used age-adjusted multivariable Cox models to investigate the interaction of stress protein with FOXO3 longevity-associated rs12212067 genotypes. For all the analyses, the p values were corrected for multiple comparisons by false discovery rate. This led to the identification of 44 stress proteins influencing the association of FOXO3 genotype with reduced mortality. Biological pathways were identified for these proteins. Our results suggest that the FOXO3 resilience genotype functions by reducing mortality in pathways related to innate immunity, bone morphogenetic protein signaling, leukocyte migration, and growth factor response.

Circulating glucuronic acid predicts healthspan and longevity in humans and mice.

Glucuronic acid is a metabolite of glucose that is involved in the detoxification of xenobiotic compounds and the structure/remodeling of the extracellular matrix. We report for the first time that circulating glucuronic acid is a robust biomarker of mortality that is conserved across species. We find that glucuronic acid levels are significant predictors of all-cause mortality in three population-based cohorts from different countries with 4-20 years of follow-up (HR=1.44, p=2.9×10-6 in the discovery cohort; HR=1.13, p=0.032 and HR=1.25, p=0.017, respectively in the replication cohorts), as well as in a longitudinal study of genetically heterogenous mice (HR=1.29, p=0.018). Additionally, we find that glucuronic acid levels increase with age and predict future healthspan-related outcomes. Together, these results demonstrate glucuronic acid as a robust biomarker of longevity and healthspan.

Gene markers of normal villous maturation and their expression in placentas with maturational pathology.

INTRODUCTION: The placenta demonstrates a recognized sequence of histomorphologic maturation throughout pregnancy, and in some cases, shows abnormally advanced (AVM) or delayed (DVM) villous maturation. While AVM and DVM have important clinical implications, it is unknown whether they truly represent a state of accelerated/delayed normal maturation or a state of pathological maldevelopment. The purpose of our study is, therefore, to address this challenge via a genome-wide search for expression markers of normal villous maturation (NM) and the assessment of these genes in cases of maturational pathology. METHODS: A total of 142 placentas, previously evaluated by gene expression microarray, were reviewed histologically and classified as NM, AVM, or DVM. Expression data from healthy NM placentas underwent Pearson correlations with gestational age (GA) and network/pathway analysis to identify candidate gene markers. Candidates were then validated in an independent microarray dataset and used to calculate "molecular GAs" of placentas with maturational pathology. RESULTS: Analysis of NM placentas yielded 17 candidate markers of normal villous maturation, of which 11 were independently validated. Genes with expression increasing across gestation were associated with transcription and metabolism, while those demonstrating decreasing expression were involved in cell cycle and division. Molecular GA was 5.3 weeks older than true GA among AVM placentas (p < 0.001), and 1.1 weeks younger among DVM placentas (p = 0.149). DISCUSSION: We have found evidence of advanced molecular GA in AVM placentas, while molecular alterations in DVM placentas were merely suggestive of delayed maturation. In the future, these findings will need to be validated with additional techniques such as in situ hybridization or immunohistochemistry.

Clinical Action Curves: Measuring the Magnitude of Physician Response to Abnormal Laboratory Results.

OBJECTIVES: While reference limits are foundational to interpreting clinical laboratory tests, they may not correspond to the actual values triggering clinical response. We propose to measure this using clinical action curves, which plot test values against an indicator of clinical action. METHODS: We selected repeat test ordering as a quantifiable, objective, useful measure that is readily calculable using available laboratory data. Using all results in Calgary in 2010-2011 for eight analytes, clinical action curves for each analyte were plotted as the relationship between index test value and retesting hazard, modeled using Cox proportional hazards with restricted cubic splines. Clinical action limits were defined where retesting hazard rose 38% above baseline (25%-50% considered). RESULTS: In general, clinical action increased before the reference limits, and clinical action limits were narrower than reference limits. However, some reference limits showed no increased clinical action and may thus be ignored in practice. CONCLUSIONS: Clinical action curves and limits provide practical, objective tools for describing physician responses to test values. Results suggest that many normal results are treated as abnormal and vice versa; such discrepancies require further scrutiny and ultimately reconciliation via altered reference ranges or altered practice patterns.

Inappropriate repeats of six common tests in a Canadian city: a population cohort study within a laboratory informatics framework.

OBJECTIVES: To identify inappropriate repeats of six common laboratory tests in a population sample of patients, using highly specific criteria based only on repeat time and test value. METHODS: We used a laboratory informatics database to conduct a retrospective cohort study using a population sample of 103,000 patients in the city of Calgary with an index test in 2010 and uniform follow-up of 1 year. We examined six tests (cholesterol, hemoglobin A1c, thyroid-stimulating hormone, vitamin B12, vitamin D, and ferritin) with consensus-based or easily justified criteria for inappropriate repeats based solely on time to repeat and the index test value. RESULTS: The percentages of tests repeated at 3, 6, and 12 months were 11%, 23%, and 41%, respectively. In total, 16% of these six tests were inappropriately repeated, representing an annual internal cost of $0.6 to $2.2 million Canadian dollars and corresponding to population-scaled national estimates for Canada and the United States of $160 million and $2.4 billion, respectively. CONCLUSIONS: Objective definitions based on repeated testing identified 16% of six studied tests as inappropriate, delineating a subset of inappropriate testing that is well suited to automated identification and intervention and that provides a likely lower bound on the true burden of inappropriate testing.

A clinical algorithm for efficient, high-resolution cytogenomic analysis of uncultured perinatal tissue samples.

PURPOSE: Cytogenetic analysis of solid tissue is indispensable in perinatal care, reproductive planning, and detection of gestational trophoblastic disease. Unfortunately, methods in common use suffer from drawbacks including culture artifact, low resolution, and high cost. We propose a new diagnostic algorithm based on direct genetic analysis of tissues (without cell culture) using QF-PCR and array CGH. METHODS: Study samples consisted of specimens submitted to the cytogenetics laboratory between January and June of 2011 that were split and analyzed in parallel by our traditional algorithm (culture and G-banding, plus an interphase FISH aneuploidy panel for culture failures) and the proposed "no-culture" algorithm (first line QF-PCR, plus array CGH on normal QF-PCRs). Data on clinical impact, cost, and turnaround time were collected. RESULTS: Forty specimens were included. The algorithms produced results that were fully concordant in 22 cases, partially concordant in 9 cases, and discordant in 9 cases. The no-culture algorithm detected new, clinically-significant abnormalities in 8 of 40 cases (20%), corrected the sex chromosome assortment in 1 case, reduced the analysis failure rate from 10% to 0%, and provided at least one of these three important benefits in 12 of 40 cases (30%). The algorithm also demonstrated a reduced cost per-specimen and per diagnosis, as well as improved turnaround time, with virtually all cases reported per guidelines. CONCLUSION: These striking results favor the "no-culture" algorithm, which may have the potential to replace standard cytogenetic methods in the clinical laboratory.

In silico drug screen in mouse liver identifies candidate calorie restriction mimetics.

Calorie restriction (CR) extends life span in mammals and delays the onset of age-related diseases, including cancer and diabetes. Drugs that target the same genes and pathways as CR may have enormous therapeutic potential. Recently, genome-scale data on the responses of human cell lines to over 1,000 drug treatments have become available. Here we integrate these data with gene expression signatures of CR in mouse liver to generate a prioritized list of candidate CR mimetics. We identify 14 drugs that reproduce the effects of CR at the transcriptional level.

The role of fine-needle aspiration in the diagnosis of thyroid lymphoma: a retrospective study of nine cases and review of published series.

AIMS: To review the clinicopathological, cytomorphological and immunophenotyping data from new cases and published series of thyroid lymphoma diagnosed by fine-needle aspiration (FNA), in order to identify useful diagnostic features. METHODS: Cases from 1988 to 2009 with an FNA diagnosis of thyroid lymphoma were selected from hospital records. An electronic MEDLINE and EMBASE search retrieved published series from 1980 to 2009. Available clinical, cytomorphological and immunophenotyping data from all cases were collected. In our cases, cytology slides and available surgical specimens were also reviewed. RESULTS: There were nine cases from eight of our patients, and 70 reviewed cases from eight series with at least four patients each. The most common presentation was a rapidly enlarging thyroid mass. Average patient age was 61 years in reviewed cases and 72 years in our cases. Large-cell lymphoma was the predominant subtype, revealing relatively monotonous populations of large, abnormal lymphoid cells. One of our cases, later diagnosed as marginal zone lymphoma, showed small lymphocytes with plasmacytoid features. Immunoprofiling information was available in five of our cases (three by immunocytochemistry and two by laser scanning cytometry) and in 34 reviewed cases (22 by immunocytochemistry, six by flow cytometry, and six by flow cytometry or immunocytochemistry). CONCLUSIONS: Cytological diagnosis of thyroid lymphoma requires careful analysis of morphological, clinical and immunophenotypic information. The presented data suggest certain helpful features: a fast-growing nodule in an elderly patient, a monotonous population of large abnormal cells in a background of lymphoglandular bodies, a predominant population of plasmacytoid lymphocytes, and immunophenotyping demonstrating light chain restriction.

Classifying B-cell non-Hodgkin lymphoma by using MIB-1 proliferative index in fine-needle aspirates.

BACKGROUND: MIB-1 proliferation index (PI) has proven helpful for diagnosis and prognosis in non-Hodgkin lymphomas (NHLs). However, validated cutoff values for use in fine-needle aspiration (FNA) samples are not available. We investigated MIB-1 immunocytochemistry as an ancillary technique for stratifying NHL and attempted to establish PI cutpoints in cytologic samples. METHODS: B-cell NHL FNA cases with available cytospins (CS) MIB-1 immunocytochemistry results were included. Demographic, molecular, immunophenotyping and MIB-1 PI data were collected from cytologic reports. Cases were subtyped according to the current World Health Organization classification and separated into indolent, aggressive, and highly aggressive groups. Statistical analysis was performed with pairwise Wilcoxon rank sum test and linear discriminant analysis to suggest appropriate PI cutpoints. RESULTS: Ninety-one NHL cases were subdivided in 56 (61.5%) indolent, 30 (33%) aggressive, and 5 (5.5%) highly aggressive lymphomas. The 3 groups had significantly different MIB-1 PIs from each other. Cutpoints were established for separating indolent (<38%), aggressive (> or =38% to < or =80.1%) and highly aggressive (>80.1%). The groups were adequately predicted in 76 cases (83.5%) using the cutpoints and 15 cases showed discrepant PIs. CONCLUSIONS: MIB-1 immunohistochemistry on CS can help to stratify B-cell NHL and showed a significant increase in PI with tumor aggressiveness. Six misclassified cases had PIs close to the cutpoints. Discrepant MIB-1 PIs were related to dilution of positive cells by non-neoplastic lymphocytes and to the overlapping continuum of features between diffuse large B-cell lymphoma and Burkitt lymphoma. Validation of our approach in an unrelated, prospective dataset is required.