Structural and functional prediction, evaluation, and validation in the post-sequencing era

The surge of genome sequencing data has underlined substantial genetic variants of uncertain significance (VUS). The decryption of VUS discovered by sequencing poses a major challenge in the post-sequencing era. Although experimental assays have progressed in classifying VUS, only a tiny fraction of the human genes have been explored experimentally. Thus, it is urgently needed to generate state-of-the-art functional predictors of VUS in silico. Artificial intelligence (AI) is an invaluable tool to assist in the identification of VUS with high efficiency and accuracy. An increasing number of studies indicate that AI has brought an exciting acceleration in the interpretation of VUS, and our group has already used AI to develop protein structure-based prediction models. In this review, we provide an overview of the previous research on AI-based prediction of missense variants, and elucidate the challenges and opportunities for protein structure-based variant prediction in the post-sequencing era.

The European biological variation study (EuBIVAS): Biological variation data for testosterone, follicle stimulating hormone, prolactin, luteinizing hormone and dehydroepiandrosterone sulfate in men

BACKGROUND

Knowledge of biological variation (BV) of hormones is essential for interpretation of laboratory tests and for diagnostics of endocrinological and reproductive diseases. There is a lack of robust BV data for many hormones in men.

METHODS

We used serum samples collected weekly over 10 weeks from the European Biological Variation Study (EuBIVAS) to determine BV of testosterone, follicle-stimulating hormone (FSH), prolactin, luteinizing hormone (LH) and dehydroepiandrosterone sulfate (DHEA-S) in 38 men. We derived within-subject (CVI) and between-subject (CVG) BV estimates by CV-ANOVA after trend, outlier, and homogeneity analysis and calculated reference change values, index of individuality (II), and analytical performance specifications.

RESULTS

The CVI estimates were 10 % for testosterone, 8 % for FSH, 13 % for prolactin, 22 % for LH, and 9 % for DHEA-S, respectively. The IIs ranged between 0.14 for FSH to 0.66 for LH, indicating high individuality.

CONCLUSIONS

In this study, we have used samples from the highly powered EuBIVAS study to derive BV estimates for testosterone, FSH, prolactin, LH and DHEA-S in men. Our data confirm previously published BV estimates of testosterone, FSH and LH. For prolactin and DHEA-S BV data for men are reported for the first time.

Variability in meta-analysis estimates of continuous outcomes using different standardization and scale-specific re-expression methods

OBJECTIVES

To explore the impact of using different data standardization and scale-specific re-expression methods (i.e., processes to convert standardized data into scale-specific units) in meta-analyses using standardized mean differences (SMDs).

STUDY DESIGN AND SETTING

We used data assessed by the Short Physical Performance Battery and the Barthel Index from a meta-analysis of randomized controlled trials which synthesized evidence of physical activity effectiveness on the functional capacity of hospitalized older adults. We standardized the data using study-specific pooled standard deviations (SDs), an internal, and an external SD references. Bayesian meta-analyses were performed for each method to compare the posterior distributions of the meta-analysis parameters. Posterior estimates were re-expressed into scale-specific units applying different methods established in the Cochrane guidelines.

RESULTS

Meta-analysis estimates depend on the used standardization method. Analyses including data standardized using the largest SD reference presented lower estimates with less uncertainty in both scales. The method applied for re-expressing SMDs into scale-specific units impacted in their posterior clinical interpretation. The most similar results across models were obtained when using the same SD reference to standardize and re-express data.

CONCLUSION

Different data standardization methods yielded different meta-analysis estimates on the SMD scale. To avoid the introduction of bias, the use of a single scale-specific SD reference to standardize data is recommended and instead of study-specific pooled sample SDs. Meta-analysis software packages may therefore change their default methods to allow this method by a single scale-specific SD. To re-express the SMDs into scale-specific units, we suggest the application of the same SD reference that was used for data standardization.

Contribution of Hemogram Plan in the Horse's Clinical Evaluation

The interpretation of the blood count is essential to help the equine clinician in the diagnosis, prognosis, patient management, and control of equine diseases. Hematologic alterations often reflect the condition of the individual or an overall response to a pathological situation. A thorough clinical examination of the patient is essential to correctly interpret the hematological results. The most common abnormalities in the erythrogram are mainly anemia and polycythemia. The frequent causes of anemia in horses are acute and chronic blood loss, hemolytic anemia, and anemia caused by chronic disease. Evaluation of leukogram, including a total white cell count, a differential cell count, absolute numbers of specific leukocytes can help identify abnormalities that may suggest specific diseases such as a viral or bacterial infection, inflammatory disorders or even a neoplastic process. The platelet count is most often used to monitor or diagnose conditions that cause too much bleeding related with thrombocytopenia; it can be due to multiple mechanisms such as reduction of thrombopoiesis (myeloptisis, myelofibrosis, myeloproliferative disease, and idiopathic medullary aplasias or due to the effect of mielosuppresive drugs), increased peripheral destruction of platelets (immune mediated thrombocytopenia), consumption (dissemined intravascular coagulation) sequestration of the spleen and loss of platelets by idiopathic origin.

Feasibility and outcome of reproducible clinical interpretation of high-dimensional molecular data: a comparison of two molecular tumor boards

BACKGROUND

Structured and harmonized implementation of molecular tumor boards (MTB) for the clinical interpretation of molecular data presents a current challenge for precision oncology. Heterogeneity in the interpretation of molecular data was shown for patients even with a limited number of molecular alterations. Integration of high-dimensional molecular data, including RNA- (RNA-Seq) and whole-exome sequencing (WES), is expected to further complicate clinical application. To analyze challenges for MTB harmonization based on complex molecular datasets, we retrospectively compared clinical interpretation of WES and RNA-Seq data by two independent molecular tumor boards.

METHODS

High-dimensional molecular cancer profiling including WES and RNA-Seq was performed for patients with advanced solid tumors, no available standard therapy, ECOG performance status of 0-1, and available fresh-frozen tissue within the DKTK-MASTER Program from 2016 to 2018. Identical molecular profiling data of 40 patients were independently discussed by two molecular tumor boards (MTB) after prior annotation by specialized physicians, following independent, but similar workflows. Identified biomarkers and resulting treatment options were compared between the MTBs and patients were followed up clinically.

RESULTS

A median of 309 molecular aberrations from WES and RNA-Seq (n = 38) and 82 molecular aberrations from WES only (n = 3) were considered for clinical interpretation for 40 patients (one patient sequenced twice). A median of 3 and 2 targeted treatment options were identified per patient, respectively. Most treatment options were identified for receptor tyrosine kinase, PARP, and mTOR inhibitors, as well as immunotherapy. The mean overlap coefficient between both MTB was 66%. Highest agreement rates were observed with the interpretation of single nucleotide variants, clinical evidence levels 1 and 2, and monotherapy whereas the interpretation of gene expression changes, preclinical evidence levels 3 and 4, and combination therapy yielded lower agreement rates. Patients receiving treatment following concordant MTB recommendations had significantly longer overall survival than patients receiving treatment following discrepant recommendations or physician's choice.

CONCLUSIONS

Reproducible clinical interpretation of high-dimensional molecular data is feasible and agreement rates are encouraging, when compared to previous reports. The interpretation of molecular aberrations beyond single nucleotide variants and preclinically validated biomarkers as well as combination therapies were identified as additional difficulties for ongoing harmonization efforts.

Clinical interpretation and implementation of the sFlt-1/PlGF ratio in the prediction, diagnosis and management of preeclampsia

Preeclampsia is associated with significant morbidity and mortality for mother and baby. Although around 30% of all pregnancies are evaluated for preeclampsia, diagnosis is difficult, especially in patients who have overlying symptoms from other diseases. Discovery of circulating angiogenic factors in the pathogenesis of preeclampsia has been a major advance for both diagnosis and prognosis. The anti-angiogenic factor, soluble fms-like tyrosine kinase 1 (sFlt-1) and the pro-angiogenic factor, placental growth factor (PlGF), can be measured in plasma and serum and are usually reported as a ratio, which specifically relates to the onset and severity of preeclampsia. The sFlt-1/PlGF ratio has a very high negative predictive value in ruling out the development of preeclampsia within 7 days among women with suspected preeclampsia. Currently, there is no clear consensus on the practical use of angiogenic biomarkers in the detection and management of preeclampsia in routine clinical practice. While major international clinical guidelines exist, they do not define which specific parameters signal patient admission, or outpatient evaluation of suspected preeclampsia, and most clinicians follow local practices. Better guidance is needed on risk stratification among women with suspected preeclampsia, as well as among women at high risk for preeclampsia. Prediction of adverse outcomes in women, after the clinical diagnosis of preeclampsia, is also important. This report has been developed following a meeting of international experts and aims to guide clinicians in the management of pregnant women at risk of preeclampsia using the sFlt-1/PlGF ratio test.

A bifactor structural model of the Hungarian Pain Catastrophizing Scale and latent classes of a clinical sample

Pain catastrophizing is an exaggerated cognitive-affective response to actual or anticipated pain, usually measured by the Pain Catastrophizing Scale (PCS). Our study aimed to test the bifactor measurement model of the Hungarian PCS and to identify a catastrophizing risk group with a clinically meaningful cut-off score.

The data of 404 chronic spine-related (neck, back and low-back) pain patients (mean age: 58.61 (SD = 14.34)) were used in our cross-sectional study. Besides pain-related and demographic data, pain catastrophizing and depressive symptoms were measured with questionnaires. Confirmatory factor analyses confirmed that the bifactor model outperformed the other tested measurement models, and the general catastrophizing factor was responsible for 81.5% of the explained variance. Using latent class analysis, we found that even moderately elevated pain catastrophizing score was related to more depressive symptoms and higher perceived pain intensity, and 22 score could be used as a cut-off score. Our results support the concept of global pain catastrophizing and the validity of the Hungarian PCS. Further studies are needed to evaluate the bifactor structure of this scale and the predictive value of the proposed cut-off score.

What the lab can and cannot do: clinical interpretation of drug testing results

Urine drug testing is one of the objective tools available to assess adherence. To monitor adherence, quantitative urinary results can assist in differentiating "new" drug use from "previous" (historical) drug use. "Spikes" in urinary concentration can assist in identifying patterns of drug use. Coupled chromatographic-mass spectrometric methods are capable of identifying very small amounts of analyte and can make clinical interpretation rather challenging, specifically for drugs that have a longer half-life. Polypharmacy is common in treatment and rehabilitation programs because of co-morbidities. Medications prescribed for comorbidities can cause drug-drug interaction and phenoconversion of genotypic extensive metabolizers into phenotypic poor metabolizers of the treatment drug. This can have significant impact on both pharmacokinetic (PK) and pharmacodynamic properties of the treatment drug. Therapeutic drug monitoring (TDM) coupled with PKs can assist in interpreting the effects of phenoconversion. TDM-PKs reflects the cumulative effects of pathophysiological changes in the patient as well as drug-drug interactions and should be considered for treatment medications/drugs used to manage pain and treat substance abuse. Since only a few enzyme immunoassays for TDM are available, this is a unique opportunity for clinical laboratory scientists to develop TDM-PK protocols that can have a significant impact on patient care and personalized medicine. Interpretation of drug screening results should be done with caution while considering pharmacological properties and the presence or absence of the parent drug and its metabolites. The objective of this manuscript is to review and address the variables that influence interpretation of different drugs analyzed from a rehabilitation and treatment programs perspective.

Navigating the nuances of clinical sequence variant interpretation in Mendelian disease

Understanding clinical genetic test results in the era of next-generation sequencing has become increasingly complex, necessitating clear and thorough guidelines for sequence variant interpretation. To meet this need the American College of Medical Genetics and Genomics (ACMG) and the Association for Molecular Pathology (AMP) published guidelines for a systematic approach for sequence variant interpretation in 2015. This framework is intended to be adaptable to any Mendelian condition, promoting transparency and consistency in variant interpretation, yet its comprehensive nature yields important challenges and caveats that end users must understand. In this review, we address some of these nuances and discuss the evolving efforts to refine and adapt this framework. We also consider the added complexity of distinguishing between variant-level interpretations and case-level conclusions, particularly in the context of the large gene panel approach to clinical diagnostics.

Pitfalls in spirometry: Clinical relevance

Spirometry is one of the functional tests most used in respiratory medicine to assess lung function in health and disease conditions. Its success is grounded on solid principles of lung mechanics that state that maximal flow on expiration is limited by the physical properties of airways and lung parenchyma. In contrast, on inspiration, flow depends on the force generated by the inspiratory muscles. Reduced expiratory forced flow and volumes usually reflect a deviation from health conditions. Yet due to a complex interplay of different obstructive and restrictive lung diseases within the multiple structural dimensions of the respiratory system, flows and volumes do not always perfectly reflect the impact of the disease on lung function. The present review is intended to shed light on a series of artefacts and biological phenomena that may confound the clinical interpretation of the main spirometric measurements. Among them is thoracic gas compression volume, the volume and time history of the inspiratory manoeuvre that precedes the forced expiration, the effects of heterogeneous distribution of the disease across the respiratory system, and the changes in lung elastic recoil.