Carlos E. Pedreira

Automated pattern-guided principal component analysis vs expert-based immunophenotypic classification of B-cell chronic lymphoproliferative disorders: a step forward in the standardization of clinical immunophenotyping

Immunophenotypic characterization of B-cell chronic lymphoproliferative disorders (B-CLPD) is becoming increasingly complex due to usage of progressively larger panels of reagents and a high number of World Health Organization (WHO) entities. Typically, data analysis is performed separately for each stained aliquot of a sample; subsequently, an expert interprets the overall immunophenotypic profile (IP) of neoplastic B-cells and assigns it to specific diagnostic categories. We constructed a principal component analysis (PCA)-based tool to guide immunophenotypic classification of B-CLPD. Three reference groups of immunophenotypic data files—B-cell chronic lymphocytic leukemias (B-CLL; n=10), mantle cell (MCL; n=10) and follicular lymphomas (FL; n=10)—were built. Subsequently, each of the 175 cases studied was evaluated and assigned to either one of the three reference groups or to none of them (other B-CLPD). Most cases (89%) were correctly assigned to their corresponding WHO diagnostic group with overall positive and negative predictive values of 89 and 96%, respectively. The efficiency of the PCA-based approach was particularly high among typical B-CLL, MCL and FL vs other B-CLPD cases. In summary, PCA-guided immunophenotypic classification of B-CLPD is a promising tool for standardized interpretation of tumor IP, their classification into well-defined entities and comprehensive evaluation of antibody panels.

Evaluation of the WHO criteria for the classification of patients with mastocytosis

Diagnosis and classification of mastocytosis is currently based on the World Health Organization (WHO) criteria. Here, we evaluate the utility of the WHO criteria for the diagnosis and classification of a large series of mastocytosis patients (n=133), and propose a new algorithm that could be routinely applied for refined diagnosis and classification of the disease. Our results confirm the utility of the WHO criteria and provide evidence for the need of additional information for (1) a more precise diagnosis of mastocytosis, (2) specific identification of new forms of the disease, (3) the differential diagnosis between cutaneous mastocytosis vs systemic mastocytosis, and (4) improved distinction between indolent systemic mastocytosis and aggressive systemic mastocytosis. Based on our results, a new algorithm is proposed for a better diagnostic definition and prognostic classification of mastocytosis, as confirmed prospectively in an independent validation series of 117 mastocytosis patients.

Overview of clinical flow cytometry data analysis: recent advances and future challenges

Major technological advances in flow cytometry (FC), both for instrumentation and reagents, have emerged over the past few decades. These advances facilitate simultaneous evaluation of more parameters in single cells analyzed at higher speed. Consequently, larger and more complex data files that contain information about tens of parameters for millions of cells are generated. This increasing complexity has challenged pre-existing data analysis tools and promoted the development of new algorithms and tools for data analysis and visualization. Here, we review the currently available (conventional and newly developed) data analysis and visualization strategies that aim for easier, more objective, and robust interpretation of FC data both in biomedical research and clinical diagnostic laboratories.

Generation of flow cytometry data files with a potentially infinite number of dimensions.

Immunophenotypic characterization of B‐cell chronic lymphoproliferative disorders (B‐CLPD) is associated with the use of increasingly larger panels of multiple combinations of 3 to ≥6 monoclonal antibodies (Mab), data analysis being separately performed for each of the different stained sample aliquots. Here, we describe and validate an automated method for calculation of flow cytometric data from several multicolor stainings of the same cell sample—i.e., the merging of data from different aliquots stained with partially overlapping combinations of Mab reagents (focusing on ≥1 cell populations)—into one data file as if it concerned a single “super” multicolor staining. Evaluation of the performance of the method described was done in a group of 60 B‐CLPD studied at diagnosis with 18 different reagents in a panel containing six different 3‐ and 4‐color stainings, which systematically contained CD19 for the identification of B‐cells. Our results show a high degree of correlation and agreement between originally measured and calculated data about cell surface stainings, providing a basis for the use of this approach for the generation of flow cytometric data files containing information about a virtually infinite number of stainings for each individual cellular event measured in a sample, using a limited number of fluorochrome stainings.

A probabilistic approach for the evaluation of minimal residual disease by multiparameter flow cytometry in leukemic B-cell chronic lymphoproliferative disorders.

Multiparameter flow cytometry has become an essential tool for monitoring response to therapy in hematological malignancies, including B‐cell chronic lymphoproliferative disorders (B‐CLPD). However, depending on the expertise of the operator minimal residual disease (MRD) can be misidentified, given that data analysis is based on the definition of expert‐based bidimensional plots, where an operator selects the subpopulations of interest. Here, we propose and evaluate a probabilistic approach based on pattern classification tools and the Bayes theorem, for automated analysis of flow cytometry data from a group of 50 B‐CLPD versus normal peripheral blood B‐cells under MRD conditions, with the aim of reducing operator‐associated subjectivity. The proposed approach provided a tool for MRD detection in B‐CLPD by flow cytometry with a sensitivity of ≤8 × 10−5 (median of ≤2 × 10−7). Furthermore, in 86% of B‐CLPD cases tested, no events corresponding to normal B‐cells were wrongly identified as belonging to the neoplastic B‐cell population at a level of ≤10−7. Thus, this approach based on the search for minimal numbers of neoplastic B‐cells similar to those detected at diagnosis could potentially be applied with both a high sensitivity and specificity to investigate for the presence of MRD in virtually all B‐CLPD. Further studies evaluating its efficiency in larger series of patients, where reactive conditions and non‐neoplastic disorders are also included, are required to confirm these results.

Birth weight patterns by gestational age in Brazil

BACKGROUND AND OBJECTIVES We present an updated birth weight-for-gestational-age portrait, based on nearly 8 million observations of an ethnic-mixed population. It comprises the first comprehensive charts with Brazilian data. This contribution intends to assist clinicians in classifying fetal growth, to provide a reference for investigations of predictors and to show the consequences of small and large patterns for gestational age delivery. Most of the reference data for assessing birth weight for gestational age deal with insufficient sample size, especially at low gestational age. Population-based studies with considerably large sample size refer to data collected more than 15 years ago. METHODS We accomplished a population-based study on births in all the Brazilian states from 2003 to 2005. Results were based on 7,993,166 singletons. We constructed the 3(rd), 5(th), 10(th), 25(th), 50(th), 90(th), 95(th) and 97(th) smoothed percentiles curves and gender-specific tables from 22 to 43 completed weeks. RESULTS The resulting tables and graphical representation provide a gender-specific reference to access the birth weights distribution according to the gestational age in the Brazilian population. CONCLUSIONS This is the first population-based reference constructed on a developing country data. These charts could provide an important tool to improve clinical assessment of growth in newborns.

A Multidimensional Classification Approach for the Automated Analysis of Flow Cytometry Data

We describe an automated multidimensional approach for the analysis of flow cytometry data based on pattern classification. Flow cytometry is a widely used technique both for research and clinical purposes where it has become essential for the diagnosis and follow up of a wide spectrum of diseases, such as HIV-infection and neoplastic disorders. Flow cytometry data sets are composed of quite a large number of observations that can be viewed as elements of a -dimensional space. The aim of the analysis of such data files is typically to classify groups of cellular events as specific populations with biological meaning. Despite significant improvements in data acquisition capabilities of flow cytometers, data analysis is still based on bi-dimensional strategies which were defined a long time ago. These are strongly dependent on the expertise of an expert operator, this approach being relatively subjective and potentially leading to unreliable results. Automated analysis of flow cytometry data is an essential step to improve reproducibility of the results. The proposed automated analysis was implemented on peripherial blood lymphocyte subsets from 307 samples stained and prepared in an identical way and it was capable of identifying all cell subsets present in each sample studied that could also be detected in the same data files by an expert operator. A highly significant correlation was found between the results obtained by an expert operator using a conventional manual method of analysis and those obtained using the implemented automated approach.

CLL-like B-lymphocytes are systematically present at very low numbers in peripheral blood of healthy adults

CLL-like B-lymphocytes are systematically present at very low numbers in peripheral blood of healthy adults

Serum Tryptase Monitoring in Indolent Systemic Mastocytosis: Association with Disease Features and Patient Outcome

Background Serum baseline tryptase (sBT) is a minor diagnostic criterion for systemic mastocytosis (SM) of undetermined prognostic impact. We monitored sBT levels in indolent SM (ISM) patients and investigated its utility for predicting disease behaviour and outcome. Methods In total 74 adult ISM patients who were followed for ≥48 months and received no cytoreductive therapy were retrospectively studied. Patients were classified according to the pattern of evolution of sBT observed. Results Overall 16/74 (22%) cases had decreasing sBT levels, 48 (65%) patients showed increasing sBT levels and 10 (13%) patients showed a fluctuating pattern. Patients with significantly increasing sBT (sBT slope ≥0.15) after 48 months of follow-up showed a slightly greater rate of development of diffuse bone sclerosis (13% vs. 2%) and hepatomegaly plus splenomegaly (16% vs. 5%), as well as a significantly greater frequency of multilineage vs. mast cells (MC)-restricted KIT mutation (p = 0.01) together with a greater frequency of cases with progression of ISM to smouldering and aggressive SM (p = 0.03), and a shorter progression-free survival (p = 0.03). Conclusions Monitoring of sBT in ISM patients is closely associated with poor prognosis disease features as well as with disease progression, pointing out the need for a closer follow-up in ISM patients with progressively increasing sBT values.

Gene expression profile of highly purified bone marrow mast cells in systemic mastocytosis

BACKGROUND Despite the fact that a great majority (>90%) of patients with systemic mastocytosis (SM) carry a common genetic lesion, the D816V KIT mutation, little is known regarding the molecular and biological pathways underlying the clinical heterogeneity of the disease. OBJECTIVE We sought to analyze the gene expression profile (GEP) of bone marrow mast cells (BMMCs) in patients with SM and its association with distinct clinical variants of the disease. METHODS GEP analyses were performed by using DNA-oligonucleotide microarrays in highly purified BMMCs from patients with SM carrying the D816V KIT mutation (n=26) classified according to the diagnostic subtype of SM versus normal/reactive BMMCs (n=7). Validation of GEP results was performed with flow cytometry in the same set of samples and in an independent cohort of 176 subjects. RESULTS Overall, 758 transcripts were significantly deregulated in patients with SM, with a common GEP (n=398 genes) for all subvariants of SM analyzed. These were characterized by upregulation of genes involved in the innate and inflammatory immune response, including interferon-induced genes and genes involved in cellular responses to viral antigens, together with complement inhibitory molecules and genes involved in lipid metabolism and protein processing. Interestingly, aggressive SM additionally showed deregulation of apoptosis and cell cycle-related genes, whereas patients with indolent SM displayed increased expression of adhesion-related molecules. CONCLUSION BMMCs from patients with different clinical subtypes of SM display distinct GEPs, which might reflect new targetable pathways involved in the pathogenesis of the disease.