Utility of icteric index in clinical laboratories: more than a preanalytical indicator

Total bilirubin tests are highly demanded in clinical laboratories. Since icteric index (I-index) has zero cost, we aimed to evaluate its clinical utility and cost-effectiveness to determine if total bilirubin is necessary to be tested. We took into account if haemolysis could interfere to icteric index determination. Retrospectively we reviewed I-index results in two cohorts (43,372 and 8507 non-haemolysed and haemolysed samples, respectively). All determinations were done using Alinity c chemistry analysers (Abbott Diagnostics). Receiver operating characteristic (ROC) curve was used to determine the optimal index cut-off to discriminate between normal and abnormal bilirubin concentration (20.5 µmol/L). The ROC curve analysis suggested 21.4 µmol/L as the optimal I-index cut-off but differences in sensitivity and specificity were detected between patient derivation. For rejecting purpose, 15.4 µmol/L and 17.1 µmol/L I-index thresholds were selected based on patient derivation (inpatients and emergency room; and primary care and outpatients, respectively) with 97% sensitivity and 0.25% false negative results. Sensitivity was much lower in haemolysed samples. We selected 34.2 µmol/L I-index as threshold to detect hyperbilirubinemia with 99.7% specificity and 0.26% false positive results, independent of haemolysis. With the icteric index cut-offs proposed, we would save 66% of total bilirubin requested and analyse total bilirubin in around 2% of samples without total bilirubin requested. This study supports the use of I-index to avoid bilirubin determination and to identify patients with hyperbilirubinemia. This work considers that the economic and test savings could help to increase the efficiency in clinical laboratories

Peripheral T-cell lymphoma: Molecular profiling recognizes subclasses and identifies prognostic markers

Peripheral T-cell lymphoma (PTCL) is a clinically aggressive disease, with a poor response to therapy and a low overall survival rate of approximately 30% after 5 years. We have analyzed a series of 105 cases with a diagnosis of PTCL using a customized NanoString platform (NanoString Technologies, Seattle, WA) that includes 208 genes associated with T-cell differentiation, oncogenes and tumor suppressor genes, deregulated pathways, and stromal cell subpopulations. A comparative analysis of the various histological types of PTCL (angioimmunoblastic T-cell lymphoma [AITL]; PTCL with T follicular helper [TFH] phenotype; PTCL not otherwise specified [NOS]) showed that specific sets of genes were associated with each of the diagnoses. These included TFH markers, cytotoxic markers, and genes whose expression was a surrogate for specific cellular subpopulations, including follicular dendritic cells, mast cells, and genes belonging to precise survival (NF-κB) and other pathways. Furthermore, the mutational profile was analyzed using a custom panel that targeted 62 genes in 76 cases distributed in AITL, PTCL-TFH, and PTCL-NOS. The main differences among the 3 nodal PTCL classes involved the RHOAG17V mutations (P < .0001), which were approximately twice as frequent in AITL (34.09%) as in PTCL-TFH (16.66%) cases but were not detected in PTCL-NOS. A multivariate analysis identified gene sets that allowed the series of cases to be stratified into different risk groups. This study supports and validates the current division of PTCL into these 3 categories, identifies sets of markers that can be used for a more precise diagnosis, and recognizes the expression of B-cell genes as an IPI-independent prognostic factor for AITL

Molecular basis of targeted therapy in T/NKcell lymphoma/leukemia: A comprehensive genomic and immunohistochemical analysis of a panel of 33 cell lines

T and NK-cell lymphoma is a collection of aggressive disorders with unfavorable outcome, in which targeted treatments are still at a preliminary phase. To gain deeper insights into the deregulated mechanisms promoting this disease, we searched a panel of 31 representative T-cell and 2 NK-cell lymphoma/leukemia cell lines for predictive markers of response to targeted therapy. To this end, targeted sequencing was performed alongside the expression of specific biomarkers corresponding to potentially activated survival pathways. The study identified TP53, NOTCH1 and DNMT3A as the most frequently mutated genes. We also found common alterations in JAK/STAT and epigenetic pathways. Immunohistochemical analysis showed nuclear accumulation of MYC (in 85% of the cases), NFKB (62%), p-STAT (44%) and p-MAPK (30%). This panel of cell lines captures the complexity of T/NK-cell lymphoproliferative processes samples, with the partial exception of AITL cases. Integrated mutational and immunohistochemical analysis shows that mutational changes cannot fully explain the activation of key survival pathways and the resulting phenotypes. The combined integration of mutational/expression changes forms a useful tool with which new compounds may be assayed

Estudio molecular de los genes de la cavernomatosis regulación de los transcritos y expresión de CCM1

Los cavernomas son malformaciones vasculares localizadas principalmente en sistema nervioso central (SNC). Estas lesiones se producen por la dilatación cavernosa de vasos sanguíneos sin ninguna intervención del parénquima nervioso. La prevalencia del 0.1-0.5% en la población general se dedujo de estudios seriados de autopsias, tanto en población Europea como Americana. La estimación real de la incidencia en la población es difícil de establecer por la penetrancia variable de esta enfermedad, habiendo muchos pacientes asintomáticos que solamente se detectarían por técnicas de imagen de resonancia magnética cerebral (MRI). Los cavernomas dan cuadros de alteraciones neurológicas locales, con crisis de epilepsia, o generales tipo cefaleas y accidentes cerebro-vasculares con hemorragia. La penetrancia clínica es muy variable y a veces es incompleta con saltos generacionales asintomáticos. La penetrancia radiológica, estudiada mediante resonancia magnética de cerebro, es más fiable en relación al status de portador o paciente. El diagnóstico clínico se confirma por MRI, realizándose actualmente mediante tecnología ecogradiente. Las características fenotípicas de los pacientes con cavernomas destacan por ser muy variables dentro de una misma generación o en distintos sujetos que comparten la misma mutación, además de las notables diferencias en la edad de comienzo e incluso la existencia de generaciones aparentemente asintomáticas. Este fenómeno hace resaltar la importancia de la patogénesis en la malformación cavernomatosa de la que serían responsable tanto factores intrínsecos como otros extrínsecos, no genéticos. En otras palabras, la malformación cavernomatosa podría deberse a la existencia de fenómenos puramente genéticos, como son las mutaciones detectadas, o bien a otros

Mutational landscape of TCL cell lines.

<p>The results of targeted deep sequencing of 16 genes in 20 T-ALL (black), 5 ALCL (dark grey), 3 CTCL (medium grey), 2 NK (light grey), 2 ATLL (diagonal lines) and one T-LGL (dots) cell lines. Mutated genes (rows) are arranged in decreasing order of mutation frequency. Cell lines (columns) are arranged from left to right on the basis of their mutational frequency following gene ranking. HTLV-1-positive cell lines (green) and translocation t(2;5)(p23;q35) (ALK +, dark blue) are showed.</p

Unsupervised hierarchical clustering analysis with 26 immunomarkers.

<p>Each row represents a single cell line; each column represents a single immunomarker. Blue (score 0); white, weak immunostaining (score 1); light red (score 2); red, strong immunoreactivity (score 3); grey, missing data.</p

Mapping of variants in a TCL gene panel.

<p>Schematic of the alterations encoded by SNVs in <i>TP53</i>, <i>NOTCH1</i>, <i>DNMT3A</i>, <i>JAK1</i>, <i>JAK3</i>, <i>STAT3</i> and <i>STAT5B</i>. Type of variation and disease are represented by color and shape, respectively. TAD: transactivation domain; PRD: proline-rich domain; TD: tetramerization domain; C-term: C-terminal domain; HD: heterodimerization domain; TM: transmembrane domain; RAM: Rbp-associated molecule domain; ANK: ankyrin domain; PEST: proline (P), glutamic acid (E), serine (S), threonine (T) degradation domain; ZNF: zinc-finger domain; Mtase: methyltransferase domain.</p