Optimal Marker Combination Research Articles

The Role of Immunohistochemistry as a Surrogate Marker in Molecular Subtyping and Classification of Bladder Cancer

Background/Objectives: Bladder cancer (BC) is a highly heterogeneous disease, presenting clinical challenges, particularly in predicting patient outcomes and selecting effective treatments. Molecular subtyping has emerged as an essential tool for understanding the biological diversity of BC; however, its implementation in clinical practice remains limited due to the high costs and complexity of genomic techniques. This review examines the role of immunohistochemistry (IHC) as a surrogate marker for molecular subtyping in BC, highlighting its potential to bridge the gap between advanced molecular classifications and routine clinical application; Methods: We explore the evolution of taxonomic classification in BC, with a particular focus on cytokeratin (KRT) expression patterns in normal urothelium, which are key to identifying basal and luminal subtypes. Furthermore, we emphasise the need for consensus on IHC markers to reliably define these subtypes, facilitating wider and standardised clinical use. The review also analyses the application of IHC in both muscle-invasive (MIBC) and non-muscle-invasive bladder cancer (NMIBC), with particular attention to the less extensively studied NMIBC cases. We discuss the practical advantages of IHC for subtyping, including its cost effectiveness and feasibility in standard pathology laboratories, alongside ongoing challenges such as the requirement for standardised protocols and external validation across diverse clinical settings; Conclusions: While IHC has limitations, it offers a viable alternative for laboratories lacking access to advanced molecular techniques. Further research is required to determine the optimal combination of markers, establish a consensus diagnostic algorithm, and validate IHC through large-scale trials. This will ultimately enhance diagnostic accuracy, guide treatment decisions, and improve patient outcomes.

A predictive and prognostic model for surgical outcome and prognosis in ovarian cancer computed by clinico-pathological and serological parameters (CA125, HE4, mesothelin).

Numerous prognostic models have been proposed for ovarian cancer, extending from single serological factors to complex gene-expression signatures. Nonetheless, these models have not been routinely translated into clinical practice. We constructed a robust and readily calculable model for predicting surgical outcome and prognosis of ovarian cancer patients by exploiting commonly available clinico-pathological factors and three selected serum parameters. Serum CA125, human epididymis protein 4 (HE4) and mesothelin (MSL) were quantified by Lumipulse® G chemiluminescent enzyme immunoassay (Fujirebio) in a total of 342 serum samples from 190 ovarian cancer patients, including 152 paired pre- and post-operative samples. Detection of pre-operative HE4 and CA125 was the optimal marker combination for blood-based prediction of surgical outcome (AUC=0.86). We constructed a prognostic model, computed by serum levels of pre-operative CA125, post-operative HE4, post-operative MSL and surgical outcome. Prognostic performance of our model was superior to any of these parameters alone and was independent from BRCA1/2 mutational status. We subsequently transformed our model into a prognostic risk index, stratifying patients as "lower risk" or "higher risk". In "higher risk" patients, relapse or death was predicted with an AUC of 0.89 and they had a significantly shorter progression free survival (HR: 9.74; 95 % CI: 5.95-15.93; p<0.0001) and overall survival (HR: 5.62; 95 % CI: 3.16-9.99; p<0.0001) compared to "lower risk" patients. We present a robust predictive/prognostic model for ovarian cancer, which could readily be implemented into routine diagnostics in order to identify ovarian cancer patients at high risk of recurrence.

CD4 and FOXP3 as predictive markers for the recurrence of T3/T4a stage II colorectal cancer: applying a novel discrete Bayes decision rule

BackgroundWe recently reported the relapse-free survival (RFS) significance of the combination of CD4+ and forkhead box P3+ (FOXP3) T-cell densities identified by immunohistochemistry in patients with stage I, II, and III colorectal cancer (CRC) who underwent curative resections. This study was designed to determine the optimal combination of markers that predict recurrence in patients with T factors of T3/T4a stage II CRC by applying a novel Bayes decision rule.MethodsUsing 137 cancer tissue specimens from T3/T4a stage II patients, 12 clinicopathologic and immune factors were analysed as predictive candidates for recurrence.ResultsOur study showed that the combination of low CD4+ and low FOXP3+ T-cell densities resulted in extremely poor RFS.ConclusionsAdjuvant chemotherapy may be considered for patients with a combination of low CD4+ and low FOXP3+ T-cell densities. The discovery of this new prognostic indicator is important for the appropriate management of patients undergoing curative resection for T3/T4a stage II CRC.

Single nucleotide polymorphism marker combinations for classifying Yeonsan Ogye chicken using a machine learning approach.

Genetic analysis has great potential as a tool to differentiate between different species and breeds of livestock. In this study, the optimal combinations of single nucleotide polymorphism (SNP) markers for discriminating the Yeonsan Ogye chicken (Gallus gallus domesticus) breed were identified using high-density 600K SNP array data. In 3,904 individuals from 198 chicken breeds, SNP markers specific to the target population were discovered through a case-control genome-wide association study (GWAS) and filtered out based on the linkage disequilibrium blocks. Significant SNP markers were selected by feature selection applying two machine learning algorithms: Random Forest (RF) and AdaBoost (AB). Using a machine learning approach, the 38 (RF) and 43 (AB) optimal SNP marker combinations for the Yeonsan Ogye chicken population demonstrated 100% accuracy. Hence, the GWAS and machine learning models used in this study can be efficiently utilized to identify the optimal combination of markers for discriminating target populations using multiple SNP markers.

A potential diagnostic serum immunological marker panel to differentiate between primary and secondary knee osteoarthritis.

Inflammation contributes to knee osteoarthritis (KOA) where many immunological mediators participate in its initiation and progression. Most clinicians manage primary (pKOA) and secondary osteoarthritis (sKOA) alike. Whether immunological profiles of pKOA and sKOA differ remains obscure. Hence, we aimed to differentially identify potential serum immunologic diagnostic markers of pKOA and of sKOA. This case control study used 46 KOA patients (pKOA, n = 30; sKOA, n = 16), and 60 age, gender matched controls (normal healthy, n = 30; systemic lupus erythematosus [SLE] disease controls, n = 30) where serum was assayed for cytokines (TNF-α, IL-1β, IL-6, IL-10) and nitric oxide derivatives (NOx). Sandwich ELISA assessed cytokine levels, while the 'Griess assay' quantified NOx levels. The diagnostic accuracy of optimal marker combinations was evaluated by the CombiROC web tool. Compared with pKOA, sKOA serum displayed significantly elevated levels of pro inflammatory cytokines (TNF-α, IL-1β, IL-6) with a concurrent decrease in the anti-inflammatory cytokine, IL-10 (P<0.05). This was reiterated by significantly higher Th1:Th2 (TNF-α: IL-10) serum cytokine ratio observed in sKOA compared to that of pKOA. The CombiROC curves identified TNF-α, IL-1β, IL-6 and NOx as the best performing panel of potential diagnostic markers to discriminate pKOA from control groups (~97% accuracy, 90% Sensitivity [SE] and 98% specificity [SP]), while TNF-α, IL-1β and IL-6 discriminated sKOA from control groups (~100% accuracy, 100% SE, and 98% SP). The study identified discrete serum immune biomarker panels to differentiate between pKOA (TNF-α, IL-1β, IL-6 and NOx) and sKOA (TNF-α, IL-1β and IL-6). These findings may assist in developing distinct therapeutic agents for the two types of KOA.

Identification of Target Chicken Populations by Machine Learning Models Using the Minimum Number of SNPs.

Simple SummaryClassifying a target population at the genetic level can provide important information for the preservation and commercial use of a breed. In this study, the minimum number of markers was used in combination, to distinguish target populations based on high-density single nucleotide polymorphism (SNP) array data. Subsequently, a genome-wide association study for filtering target-population-specific SNPs between the case and control groups and principal component analysis with machine learning algorithms could be used to explore various combinations with the minimum number of markers. In addition, the optimal combination of SNP markers was able to produce stable results for the target population in verification studies, in which samples were analyzed.A marker combination capable of classifying a specific chicken population could improve commercial value by increasing consumer confidence with respect to the origin of the population. This would facilitate the protection of native genetic resources in the market of each country. In this study, a total of 283 samples from 20 lines, which consisted of Korean native chickens, commercial native chickens, and commercial broilers with a layer population, were analyzed to determine the optimal marker combination comprising the minimum number of markers, using a 600 k high-density single nucleotide polymorphism (SNP) array. Machine learning algorithms, a genome-wide association study (GWAS), linkage disequilibrium (LD) analysis, and principal component analysis (PCA) were used to distinguish a target (case) group for comparison with control chicken groups. In the processing of marker selection, a total of 47,303 SNPs were used for classifying chicken populations; 96 LD-pruned SNPs (50 SNPs per LD block) served as the best marker combination for target chicken classification. Moreover, 36, 44, and 8 SNPs were selected as the minimum numbers of markers by the AdaBoost (AB), Random Forest (RF), and Decision Tree (DT) machine learning classification models, which had accuracy rates of 99.6%, 98.0%, and 97.9%, respectively. The selected marker combinations increased the genetic distance and fixation index (Fst) values between the case and control groups, and they reduced the number of genetic components required, confirming that efficient classification of the groups was possible by using a small number of marker sets. In a verification study including additional chicken breeds and samples (12 lines and 182 samples), the accuracy did not significantly change, and the target chicken group could be clearly distinguished from the other populations. The GWAS, PCA, and machine learning algorithms used in this study can be applied efficiently, to determine the optimal marker combination with the minimum number of markers that can distinguish the target population among a large number of SNP markers.

Abstract IA08: Incorporating proteomics in bladder cancer molecular diagnosis

Abstract Proteins are directly linked to cell phenotype, hence accrue interest as biomarkers and therapeutic targets. Investigating the urine proteome as a source of biomarkers entails multiple technical challenges, with the main being the protein concentration vast dynamic range and presence of proteins in multiple modified forms. As a result, protein enrichment strategies in combination to different mass spectrometry platforms are regularly employed as main discovery tools, providing complementary experimental outputs. Verification and further biomarker validation regularly involve application of ELISA and, more recently, multiplex mass spectrometry (MS)-based assays (Multiple Reaction Monitoring–MS). In the course of this presentation, an overview of urine proteomics, as applied in bladder cancer research, will be provided. In brief, using the armamentarium of existing methodologic approaches, multiple urinary proteins have been associated with bladder cancer phenotypes and/or attributed prognostic or predictive value, including the Nuclear Matrix Protein 22 (NMP22) and Bladder Tumor Antigen (BTA; corresponding to complement factor H-related protein), approved by the FDA for use in disease diagnosis and/or surveillance. Additional proteins frequently reported in the literature as associated with bladder cancer detection and/or staging include various forms of apolipoproteins, proteins of the extracellular matrix (such as collagen and fibrinogen fragments), matrix metalloproteinases, cytokeratin fragments as well as proteins involved in apoptosis (such as survivin) or inflammation (including multiple cytokines). Reports also support potential of some of these proteins when evaluated in the form of biomarker panels, as predictors of BCG response in high-risk non-muscle invasive bladder cancer. Along these lines, simultaneous analysis of multiple markers through ELISA or mass spectrometry-based platforms has provided increased accuracies in comparison to single biomarkers. Hematuria is a frequent confounding variable in urinary protein measurements, rendering establishment of signal specificity via optimized ELISA assays or mass spectrometry platforms a current technological target. Direct links of urinary protein changes to bladder tissue events have been frequently reported (such as the cases of profilin 1 or activated leukocyte cell adhesion molecule). Even though disproportionate to the numerous reported findings (frequently compromised by suboptimal experimental design), collectively, promising urinary protein findings in association to bladder cancer phenotypes and contexts of use are existent, awaiting proper validation in large-scale prospective trials. Main research paths that now open up include: refinement of existing bladder cancer molecular subtypes, with the addition of information at the protein level, and placing urinary markers in this context; and, in parallel, design and running of proper validation studies as well as clinical sample sharing, to define optimal marker combinations per clinically relevant bladder cancer context of use. In the latter, special emphasis should be placed on prediction of treatment response, in order to maximize benefit from available treatment modalities. Citation Format: Antonia Vlahou. Incorporating proteomics in bladder cancer molecular diagnosis [abstract]. In: Proceedings of the AACR Special Conference on Bladder Cancer: Transforming the Field; 2019 May 18-21; Denver, CO. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(15_Suppl):Abstract nr IA08.

Application of latent class analysis in diagnosis of graft-versus-host disease by serum markers after allogeneic haematopoietic stem cell transplantation

Graft-versus-host disease (GVHD) is one of the major causes of morbidity and mortality in 25–70% of patients. The gold standard (GS) test to confirm the diagnosis of GVHD has some limitations. The current study was conducted to evaluate the accuracy of three serum markers in diagnosing GVHD without a GS. 94 patients who were hospitalized for allogeneic transplantation were studied. Mean levels from day of haematopoietic stem cell transplantation (HSCT) to discharge of serum uric acid (UA), lactate dehydrogenase (LDH), and creatinine (Cr) were measured for all participants. We adapted a Bayesian latent class analysis to modelling the results of each marker and combination of markers. The Sensitivity, Specificity, and area under receiver operating characteristic curve (AUC) for LDH were as 51%, 81%, and 0.70, respectively. For UA, the Sensitivity, Specificity, and AUC were 54%, 75%, and 0.71, respectively. The estimated Sensitivity, Specificity, and AUC of Cr were 72%, 94%, and 0.86, respectively. Adjusting for covariates, the combined Sensitivity, Specificity, and AUC of the optimal marker combination were 76%, 83%, and 0.94, respectively. To conclude, our findings suggested that Cr had the strongest diagnosis power for GVHD. Moreover, the classification accuracy of the three-marker combination outperforms the other combinations.

Selected serum cytokines and nitric oxide as potential multi-marker biosignature panels for Parkinson disease of varying durations: a case-control study

BackgroundDopaminergic neuronal loss begins years before motor symptoms appear in Parkinson disease (PD). Thus, reliable biomarkers for early diagnosis and prognosis of PD are an essential pre-requisite to develop disease modifying therapies. Inflammation-derived oxidative stress is postulated to contribute to nigrostriatal degeneration. We evaluated the role of selected serum immune mediators (IFNγ, TNFα, IL-10, and NOx) in PD progression and estimated their usefulness in preclinical diagnosis.MethodsThis case-control study recruited 72 PD patients with varying disease durations (< 1-year, n = 12 patients; 1–3 years, n = 30; > 3 years, n = 30) and 56 age- and gender-matched controls (26 with other neurological disorders as disease controls, and 30 healthy controls). Serum cytokine levels and NOx quantified using Sandwich Enzyme Linked Immunosorbent Assay kits, and the Griess test, respectively, were evaluated for diagnostic accuracy of optimal marker combinations by the CombiROC method. PD patients were clinically evaluated for motor and non-motor symptoms, and staged based on Hoehn and Yahr (H-Y) scale.ResultsA significant increase in serum IFNγ and IL-10 was observed in PD compared to healthy controls (p < 0.001). The Th1: Th2 (IFNγ: IL-10) cytokine ratio was higher in PD of 3–12 years compared with PD < 1 year (p < 0.001). Highest levels of NOx manifested during early PD (1–3 years) through a subsequent decline with disease duration. TNFα level was highest at PD onset. A low serum NOx level was associated with cognitive impairment (p = 0.002). The potential of using multi-biomarker panel, IFNγ, IL-10 and TNFα, for detection of PD onset was evident (sensitivity [SE] = 83.3%, specificity [SP] =80.4%, area under curve [AUC] = 0.868), while for early and late PD the multi-biomarker signature of IFNγ, IL-10 and NOx appeared to be more promising (SE = 93.3%, SP = 87.5%, AUC = 0.924).ConclusionA Th1 cytokine-biased immune response predominates with PD progression. Both IFNγ and IL-10 are involved in disease severity. However, TNFα-mediated neurotoxicity appears to occur in early PD.

DNS study of the optimal heat release rate marker in premixed methane flames

The objective of this study is to identify a chemical marker combination that can be measured by Laser-Induced Fluorescence and used for predicting the heat release rate distribution in premixed methane-air flames at various equivalence ratios. Direct Numerical Simulations of turbulent methane flames at different equivalence ratios have been performed to obtain an indirect but accurate estimation of heat release based on all possible chemical markers. Two detailed kinetic mechanisms have been considered to check that a robust estimation is obtained. The simulation results are post-processed and analyzed by a pixel-to-pixel image comparison. At the difference of other studies, the normalized distribution of heat release and the reconstruction based on chemical markers are compared directly, so that it is possible to investigate as well geometric flame parameters, like flame thickness, for specific regions of heat release. Optimal marker combinations for measurable species have been combined with corresponding exponents. Finally, for methane flames with an equivalence ratio ranging from 0.6 to 1.4, it is found that the marker combination ([OH]1.07 × [CH2O]1.17) (where [ ] denotes the normalized concentration) leads to a very high reconstruction quality for heat release, particularly so at high levels of heat release. Alternatively, ([H]1.11 × [CH2O]0.86) leads to an even better approximation of the whole heat release field. However, OH-based markers are easier to measure with a good accuracy.

ПОКАЗНИКИ ГЕМОСТАТИЧНОГО ПОТЕНЦІАЛУ НА РІЗНИХ СТАДІЯХ РАКУ СЕЧОВОГО МІХУРА

Bladder cancer is characterized by a high mortality rate and is the 9th most common cancerous disease in the world. With a wide array of diagnostic bases, which include cystoscopy, ultrasound, computer and magnetic resonance imaging, cytology of urine deposition, it is difficult to accurately determine the extent of development of the tumor. It is important to find molecular and biochemical predictive parameters that would be characterized by high specificity and sensitivity to the tumor development. According to modern scientific data, an imbalance in the coagulation system is observed in cancer patients, the manifestation of which is the activation of the blood clotting system. Activation of the hemostatic system is accompanied by the appearance in the bloodstream of specific markers that reflect increase the hemostatic potential of the blood, such as the level of fibrin, fibrinogen, prothrombin time, activated partial thromboplastin time, activated plasma recalcification time. The results of a study of various cancers indicate an increased level of components of the plasminogen activation system, including the inhibitor of plasminogen activator-1 (PAI-1). The literature data that would indicate the prognostic significance of changes in the parameters of the blood clotting system and the proteolytic system in the blood in bladder cancer are few and have not completed. There are no data indicating the link between the studied parameters with histopathological gradation. In a detailed review of markers, it is possible to identify the optimal combination of markers of pathological processes, including the pathogenesis of oncological processes at various stages. During the study, we have investigated a significant increase in the level of fibrin, fibrinogen, and PAI-1, depending on the degree of bladder cancer. The test scores can make a significant contribution to the characterization of bladder cancer, depending on the grade according to the histopathological classification.

Molecular markers of putative spermatogonial stem cells in the domestic cat

Spermatogonial stem cells (SSCs) are an important tool for fertility preservation and species conservation. The ability to expand SSCs by in vitro culture is a crucial premise for their use in assisted reproduction. Because SSCs represent a small proportion of the germ cells in the adult testis, culture success is aided by pre-enrichment through sorting techniques based on cell surface-specific markers. Given the importance of the domestic cat as a model for conservation of endangered wild felids, herein we sought to examine culture conditions as well as molecular markers for cat SSCs. Using a cell culture medium for mouse SSCs supplemented with glial cell-derived neurotrophic factor (GDNF), germ cells from prepuberal cat testes remained viable in culture for up to 43days. Immunohistochemistry for promyelocytic leukaemia zinc finger (PLZF) protein on foetal, prepuberal and adult testis sections revealed a pattern of expression consistent with the labelling of undifferentiated spermatogonia. Fluorescence-activated cell sorting (FACS) with an antibody against epithelial cell adhesion molecule (EPCAM) was used to sort live cells. Then, the gene expression profile of EPCAM-sorted cells was investigated through RT-qPCR. Notably, EPCAM (+) cells expressed relatively high levels of CKIT (CD117), a surface protein typically expressed in differentiating germ cells but not SSCs. Conversely, EPCAM (-) cells expressed relatively high levels of POU domain class 5 transcription factor 1 (POU1F5 or OCT4), clearly a germ line stem cell marker. These results suggest that cat SSCs would probably be found within the population of EPCAM (-) cells. Future studies should identify additional surface markers that alone or in combination can be used to further enrich SSCs from cat germ cells.

Multiplex pyrosequencing of InDel markers for forensic DNA analysis.

The capillary electrophoresis (CE) technology is commonly used for fragment length separation of markers in forensic DNA analysis. In this study, pyrosequencing technology was used as an alternative and rapid tool for the analysis of biallelic InDel (insertion/deletion) markers for individual identification. The DNA typing is based on a subset of the InDel markers that are included in the Investigator® DIPplex Kit, which are sequenced in a multiplex pyrosequencing analysis. To facilitate the analysis of degraded DNA, the polymerase chain reaction (PCR) fragments were kept short in the primer design. Samples from individuals of Swedish origin were genotyped using the pyrosequencing strategy and analysis of the Investigator® DIPplex markers with CE. A comparison between the pyrosequencing and CE data revealed concordant results demonstrating a robust and correct genotyping by pyrosequencing. Using optimal marker combination and a directed dispensation strategy, five markers could be multiplexed and analyzed simultaneously. In this proof-of-principle study, we demonstrate that multiplex InDel pyrosequencing analysis is possible. However, further studies on degraded samples, lower DNA quantities, and mixtures will be required to fully optimize InDel analysis by pyrosequencing for forensic applications. Overall, although CE analysis is implemented in most forensic laboratories, multiplex InDel pyrosequencing offers a cost-effective alternative for some applications.

Abstract B45: A novel reporter system with potential for in situ assessment of tumor microenvironmental effects on cancer stem cells

Abstract Many tumors consist of a hierarchy of cells with different proliferative and developmental potential. A small number of cancer stem cells (CSCs) give rise to a larger population of highly proliferative, committed progenitor cells, which may then undergo limited differentiation. Importantly, CSCs are uniquely capable of initiating and sustaining tumorigenesis, and they have been implicated in driving disease recurrence after cancer therapy. Thus understanding CSC biology will be critical to the development of more effective therapies. CSCs are most commonly identified by FACS analysis but the optimal marker combinations are very dependent on the tissue and specific cell-of-origin of the tumor, and they cannot be used to monitor the CSCs in situ, with all the microenvironmental cues intact. Such markers cannot readily be used for real-time assessment of stem cell behavior at a single cell rather than a population level. To address this problem, we have developed and validated a novel lentiviral-based reporter system for direct visualization, quantitation and isolation of the cells with CSC properties. The construct consists of a tandemly repeated composite Sox2-Oct4 response element (SORE6) driving expression of a destabilized green fluorescent protein reporter. The reporter responds to the presence of the core stem cell transcription factors Oct4 and Sox2, with further stem cell selectivity and kinetic resolution coming from the use of a proteosome-targeting degron on the fluorescent protein. Using the human MCF10CA1h breast cancer cell line, we have shown that SORE6-GFP+ cells within the cell population are undifferentiated and enriched for stem cell markers. These cells can self-renew and regenerate GFP- cells, show enhanced asymmetric division, and are enriched for tumorsphere formation in vitro. Most importantly the SORE6-GFP+ cells are enriched for tumor-initiating and metastasis-initiating ability in vivo and they are relatively resistant to chemotherapeutics both in vitro and in vivo. Thus by a number of criteria, the reporter is marking a cell population that is substantially enriched for CSCs. The reporter works in primary human breast cancer cultures and patient-derived xenografts in addition to established cell line models. Our novel imaging approach opens up the possibility of assessing the spatial distribution of CSCs and temporal changes in CSC properties in experimental settings that retain the complex microenvironmental and structural cues of the tumor bed. Citation Format: Binwu Tang, Asaf Raviv, Dominic Esposito, Catherine Daniel, Bao Tram Nghiem, Susan Garfield, Langston Lim, Poonam Mannan, Ana Robles, William Smith, Joshua Zimmerberg, Rea Ravin, Lalage Wakefield. A novel reporter system with potential for in situ assessment of tumor microenvironmental effects on cancer stem cells. [abstract]. In: Abstracts: AACR Special Conference on Cellular Heterogeneity in the Tumor Microenvironment; 2014 Feb 26-Mar 1; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(1 Suppl):Abstract nr B45. doi:10.1158/1538-7445.CHTME14-B45

AucPR: an AUC-based approach using penalized regression for disease prediction with high-dimensional omics data.

MotivationIt is common to get an optimal combination of markers for disease classification and prediction when multiple markers are available. Many approaches based on the area under the receiver operating characteristic curve (AUC) have been proposed. Existing works based on AUC in a high-dimensional context depend mainly on a non-parametric, smooth approximation of AUC, with no work using a parametric AUC-based approach, for high-dimensional data.ResultsWe propose an AUC-based approach using penalized regression (AucPR), which is a parametric method used for obtaining a linear combination for maximizing the AUC. To obtain the AUC maximizer in a high-dimensional context, we transform a classical parametric AUC maximizer, which is used in a low-dimensional context, into a regression framework and thus, apply the penalization regression approach directly. Two kinds of penalization, lasso and elastic net, are considered. The parametric approach can avoid some of the difficulties of a conventional non-parametric AUC-based approach, such as the lack of an appropriate concave objective function and a prudent choice of the smoothing parameter. We apply the proposed AucPR for gene selection and classification using four real microarray and synthetic data. Through numerical studies, AucPR is shown to perform better than the penalized logistic regression and the nonparametric AUC-based method, in the sense of AUC and sensitivity for a given specificity, particularly when there are many correlated genes.ConclusionWe propose a powerful parametric and easily-implementable linear classifier AucPR, for gene selection and disease prediction for high-dimensional data. AucPR is recommended for its good prediction performance. Beside gene expression microarray data, AucPR can be applied to other types of high-dimensional omics data, such as miRNA and protein data.

ROC Analysis in Biomarker Combination with Covariate Adjustment

Receiver operating characteristic (ROC) analysis is often used to find the optimal combination of biomarkers. When the subject level covariates affect the magnitude and/or accuracy of the biomarkers, the combination rule should take into account of the covariate adjustment. The authors propose two new biomarker combination methods that make use of the covariate information. The first method is to maximize the area under the covariate-adjusted ROC curve (AAUC). To overcome the limitations of the AAUC measure, the authors further proposed the area under covariate-standardized ROC curve (SAUC), which is an extension of the covariate-specific ROC curve. With a series of simulation studies, the proposed optimal AAUC and SAUC methods are compared with the optimal AUC method that ignores the covariates. The biomarker combination methods are illustrated by an example from Alzheimer's disease research. The simulation results indicate that the optimal AAUC combination performs well in the current study population. The optimal SAUC method is flexible to choose any reference populations, and allows the results to be generalized to different populations. The proposed optimal AAUC and SAUC approaches successfully address the covariate adjustment problem in estimating the optimal marker combination. The optimal SAUC method is preferred for practical use, because the biomarker combination rule can be easily evaluated for different population of interest.

Combined flow cytometric assessment of CD45, HLA‐DR, CD34, and CD117 expression is a useful approach for reliable quantification of blast cells in myelodysplastic syndromes

Quantification of bone marrow (BM) blasts by cytomorphology is essential for the diagnosis of myelodysplastic syndromes (MDS). Owing to its subjectivity and the potential impact of dysplastic features on accurate identification of blast cells, more objective approaches are required, multiparameter flow cytometry (MFC) being a particularly promising approach in this regard. However, no consensus exists about the optimal combination of markers and strategy to be used. BM blast counts from 74 MDS patients were evaluated by morphology versus four different MFC phenotypic criteria: "CD34⁺", "CD34⁺ and/or CD117⁺", "CD34⁺, and/or CD117⁺ HLA-DR⁺", and "CD34⁺ and CD117⁺ HLA-DR⁺ plus CD64⁺ CD14(-/lo) " cells. For each criterium, the percentage of blasts was calculated using either all BM nucleated cells or non-erythroid CD45⁺ cells as denominator. RESULTS.: The number of "CD34⁺ and/or CD117⁺ HLA-DR⁺"cells showed the highest correlation and agreement with morphological counts, only a minor proportion of cases being misclassified by MFC vs. morphology for the >5% and >10% classification thresholds. In turn, a CD34⁺ phenotype was insufficient to correctly identify and quantify blasts. Conversely, usage of non-erythroid BM cells as denominator, or inclusion of "CD34⁺ and/or CD117⁺ HLA-DR⁺ plus CD64⁺ CD14(-lo") cells were both associated with overestimated blast counts. Quantification of "CD34⁺ and/or CD117⁺ HLA-DR⁺" cells (from all nucleated BM cells) by MFC is an efficient method for the enumeration of blasts in MDS. However, caution should be taken with replacing morphology by MFC blast counts; its combined use may rather provide complementary information increasing the accuracy and reproducibility of BM blast cell counts in these patients.

Microarray Screening for Novel Preeclampsia Biomarker Candidates

Introduction: Our aim was to identify novel biomarker candidates for the near-term prediction of preeclampsia in a homogenous collective. In this study, we screened at the genome-wide level for gene expression in placental villous tissue from patients with severe preeclampsia in comparison to normal healthy pregnancies. Material and Methods: Total RNA was extracted from placental villous tissue from 9 preeclamptic patients and 7 normotensive controls after scheduled cesarean sections. After sample pooling, gene expression analysis was performed using six Affymetrix Human Gene 1.0 ST arrays, followed by quantitative RT-PCR and validation of selected markers in the serum of patients at the protein level. Results: In total, 896 significantly differentially expressed genes were identified (p ≤ 0.05). After restricting these to molecules present in the circulation, 9 upregulated and 5 downregulated genes were selected. Four of them (β-hCG, HTRA4, LHB1, all upregulated; and NOX4, downregulated) were validated by quantitative real-time RT-PCR. Finally, the maternal plasma protein levels of 2 of these genes (LHB and β-hCG) were confirmed to be significantly different between preeclampsia cases and controls. Discussion: We identified 14 potential new biomarker candidates for preeclampsia and validated 4 of them by quantitative RT-PCR and 2 of them with subsequent serum protein analyses. Further studies will assess the optimal marker combination for the imminent prediction of impending preeclampsia.

OP27.02: Biochemical maternal serum markers alone and in combination predict extremes of Doppler abnormality (AREDV)

Objectives: Maternal serum markers and Doppler velocimetry of the umbilical artery are independent assessments of placental function. Our goal was to evaluate a relationship between maternal serum biochemical markers and extreme Doppler abnormalities of the umbilical artery-absent or reversed end diastolic velocity (AREDV). Methods: A retrospective analysis was performed on 68 patients with AREDV at 18–39 wks who had 1st (1T) and/or 2nd (2T) trimester trisomy screening along with 3700 controls who had 1T and 2T screening. The optimal combination of markers using continuous log MoM values was determined using stepwise logistic regression. Screening performance was evaluated based on modeling of log Gaussian Distributions. Results: Median MoMs in the AREDV group were: free beta hCG (1T)-1.07, PAPP-A-0.52, AFP-1.27, free beta hCG (2T)-1.45, unconjugated estriol (UE3)-0.74 and inhibin-2.15. Except for free beta hCG (FBhCG)-1T, all were significantly different from controls (P 95th percentile. A combination of PAPP-A, inhibin, AFP and UE3 log MoMs significantly predicted risk. Table 1 shows screening performance. Conclusions: Early knowledge of pregnancies that go on to have AREDV may have significant clinical implications. Our data indicate that a combination of maternal serum markers identifies up to 75% of these pregnancies. Such an approach may provide a more precise risk assessment for adverse pregnancy outcomes such as pre-eclampsia, IUGR and stillbirth.

Estimation for the Optimal Combination of Markers without Modeling the Censoring Distribution

In the time-dependent receiver operating characteristic curve analysis with several baseline markers, research interest focuses on seeking appropriate composite markers to enhance the accuracy in predicting the vital status of individuals over time. Based on censored survival data, we proposed a more flexible estimation procedure for the optimal combination of markers under the validity of a time-varying coefficient generalized linear model for the event time without restrictive assumptions on the censoring pattern. The consistency of the proposed estimators is also established in this article. In contrast, the inverse probability weighting (IPW) approach might introduce a bias when the selection probabilities are misspecified in the estimating equations. The performance of both estimation procedures are examined and compared through a class of simulations. It is found from the simulation study that the proposed estimators are far superior to the IPW ones. Applying these methods to an angiography cohort, our estimation procedure is shown to be useful in predicting the time to all-cause and coronary artery disease related death.