Prediction and precise diagnosis of diseases by data pattern analysis in multiparameter flow cytometry: melanoma, juvenile asthma, and human immunodeficiency virus infection

A survey of flow cytometry data analysis methods

Flow cytometry (FCM) is widely used in health research and in treatment for a variety of tasks, such as in the diagnosis and monitoring of leukemia and lymphoma patients, providing the counts of helper-T lymphocytes needed to monitor the course and treatment of HIV infection, the evaluation of peripheral blood hematopoietic stem cell grafts, and many other diseases. In practice, FCM data analysis is performed manually, a process that requires an inordinate amount of time and is error-prone, nonreproducible, nonstandardized, and not open for re-evaluation, making it the most limiting aspect of this technology. This paper reviews state-of-the-art FCM data analysis approaches using a framework introduced to report each of the components in a data analysis pipeline. Current challenges and possible future directions in developing fully automated FCM data analysis tools are also outlined.

Multi-target spectral moment: QSAR for antiviral drugs vs. different viral species

The antiviral QSAR models have an important limitation today. They predict the biological activity of drugs against only one viral species. This is determined by the fact that most of the current reported molecular descriptors encode only information about the molecular structure. As a result, predicting the probability with which a drug is active against different viral species with a single unifying model is a goal of major importance. In this work, we use Markov Chain theory to calculate new multi-target spectral moments to fit a QSAR model for drugs active against 40 viral species. The model is based on 500 drugs (including active and non-active compounds) tested as antiviral agents in the recent literature; not all drugs were predicted against all viruses, but only those with experimental values. The database also contains 207 well-known compounds (not as recent as the previous ones) reported in the Merck Index with other activities that do not include antiviral action against any virus species. We used Linear Discriminant Analysis (LDA) to classify all these drugs into two classes as active or non-active against the different viral species tested, whose data we processed. The model correctly classifies 5129 out of 5594 non-active compounds (91.69%) and 412 out of 422 active compounds (97.63%). Overall training predictability was 92.34%. The validation of the model was carried out by means of external predicting series, the model classifying, thus, 2568 out of 2779 non-active compounds and 224 out of 229 active compounds. Overall training predictability was 92.82%. The present work reports the first attempts to calculate within a unified framework the probabilities of antiviral drugs against different virus species based on a spectral moment analysis.

Cytomics as a new potential for drug discovery

At the single-cell level in conjunction with data-pattern analysis, high-content screening by image analysis or flow cytometry of clinical cell- or tissue-section samples provides differential molecular profiles for the personalized prediction of therapy-dependent disease progression in patients. The molecular reverse-engineering of these molecular profiles, which is the exploration of molecular pathways, backwards, to the origin of the observed molecular differentials, by systems biology has the potential to detect new drug targets in knowledge spaces, typically inaccessible to traditional hypotheses. Furthermore, predictive medicine, by cytomics in stratified patient groups, opens a new way for personalized (or individualized) medicine, as well as for the early detection of adverse drug reactions in patients.

Pretherapeutic identification of high-risk acute myeloid leukemia (AML) patients from immunophenotypic, cytogenetic, and clinical parameters

BACKGROUND

The goal of this study concerned the pretherapeutic identification of high-risk acute myeloid leukemia (AML) patients by data pattern analysis from flow cytometric immunophenotype, cytogenetic, and clinical data.

METHODS

Sixty-seven parameters of AML patients at diagnosis were classified for predictive information by algorithmic data sieving using iteratively self optimizing triple matrix data pattern analysis (http://www.biochem.mpg.de/valet/classif1.html).

RESULTS

Pretherapeutic predictive values for nonsurvival within five years and two years were 100.0% and 83.2%, respectively, compared to 13.9% and 47.4% for the prediction of survival at five years and two years, respectively. At diagnosis, five-year nonsurvivors showed increased patient age and higher concentration of cells in the analyzed specimen, as well as increased levels of % CD2, CD4, CD13, CD36, and CD45 positive AML blasts. Two-year nonsurvivors were characterized by a data pattern of increased patient age and levels of % CD4, CD7, CD11b, CD24, CD45, TH126, and HLA-DR positive AML blasts and decreased levels of % CD1, CD65, CD95, and TC25 positive AML blasts. Cytogenetic abnormalities were not selected for the optimized discriminatory data patterns.

CONCLUSIONS

The comparatively accurate pretherapeutic identification of high-risk AML patients may prove useful for the development of individualized therapy protocols in stratified clinical patients groups.

Preoperative prediction of postoperative edema and effusion in pediatric cardiac surgery by altered antigen expression patterns on granulocytes and monocytes

Postoperative edema and effusion (POEE) following cardiopulmonary bypass (CPB) surgery in children retards recovery and may aggravate postpericardiotomy (PPS), capillary leak syndrome (CLS), or multiorgan failure (MOF). Compared with complication-free children, POEE affected children have different preoperative serum levels of circulating cytokines and adhesion molecules. These levels may be used preoperatively to assess POEE, but their determination is time consuming, costly, and a substantial blood volume is required. Altered serum levels of cytokines and adhesion molecules also may be reflected in altered antigen expression on circulating blood leukocytes. The predictive potential of flow cytometric (FCM) leukocyte immunophenotyping was explored as a sensitive and fast method that required small blood samples. Blood samples taken 24 h preoperatively from 49 patients (3-18 years old) were stained with monoclonal antibodies for adhesion molecules (ICAM-1, LFA-1, Mac-1) or constitutive/activation markers (CD4, CD14, CD16, CD25, CD54, CD69, HLA-DR) and measured on a microbead calibrated FCM. Neutrophils, monocytes, and eosinophils from POEE patients express higher preoperative levels of LFA-1, monocytes, HLA-DR, and other activation markers (all P < 0.03). Over 89% of the patients were classified correctly by using two discriminant analysis methods (sensitivity, >76%; specificity, >86%; positive prediction, >80%; negative prediction, >83%). Granulocytes and monocytes of postoperative POEE patients exhibit significant preoperative immune activation, suggesting an increased risk for patients with atopic/allergic predisposition. Surgical trauma and CPB cause additional immune activation, leading to POEE by a summative response. Most patients at risk for POEE can be identified preoperatively by using data pattern analysis on FCM-derived parameters.