Single-cell multiomics guided mechanistic understanding of Fontan-associated liver disease.

The Fontan operation is the current standard of care for single-ventricle congenital heart disease. Individuals with a Fontan circulation (FC) exhibit central venous hypertension and face life-threatening complications of hepatic fibrosis, known as Fontan-associated liver disease (FALD). The fundamental biology and mechanisms of FALD are little understood. Here, we generated a transcriptomic and epigenomic atlas of human FALD at single-cell resolution using multiomic snRNA-ATAC-seq. We found profound cell type-specific transcriptomic and epigenomic changes in FC livers. Central hepatocytes (cHep) exhibited the most substantial changes, featuring profound metabolic reprogramming. These cHep changes preceded substantial activation of hepatic stellate cells and liver fibrosis, suggesting cHep as a potential first "responder" in the pathogenesis of FALD. We also identified a network of ligand-receptor pairs that transmit signals from cHep to hepatic stellate cells, which may promote their activation and liver fibrosis. We further experimentally demonstrated that activins A and B promote fibrotic activation in vitro and identified mechanisms of activin A's transcriptional activation in FALD. Together, our single-cell transcriptomic and epigenomic atlas revealed mechanistic insights into the pathogenesis of FALD and may aid identification of potential therapeutic targets.

A longitudinal single-cell and spatial multiomic atlas of pediatric high-grade glioma.

Pediatric high-grade glioma (pHGG) is an incurable central nervous system malignancy that is a leading cause of pediatric cancer death. While pHGG shares many similarities to adult glioma, it is increasingly recognized as a molecularly distinct, yet highly heterogeneous disease. In this study, we longitudinally profiled a molecularly diverse cohort of 16 pHGG patients before and after standard therapy through single-nucleus RNA and ATAC sequencing, whole-genome sequencing, and CODEX spatial proteomics to capture the evolution of the tumor microenvironment during progression following treatment. We found that the canonical neoplastic cell phenotypes of adult glioblastoma are insufficient to capture the range of tumor cell states in a pediatric cohort and observed differential tumor-myeloid interactions between malignant cell states. We identified key transcriptional regulators of pHGG cell states and did not observe the marked proneural to mesenchymal shift characteristic of adult glioblastoma. We showed that essential neuromodulators and the interferon response are upregulated post-therapy along with an increase in non-neoplastic oligodendrocytes. Through in vitro pharmacological perturbation, we demonstrated novel malignant cell-intrinsic targets. This multiomic atlas of longitudinal pHGG captures the key features of therapy response that support distinction from its adult counterpart and suggests therapeutic strategies which are targeted to pediatric gliomas.

Identification and targeting of treatment resistant progenitor populations in T-cell Acute Lymphoblastic Leukemia.

Refractoriness to initial chemotherapy and relapse after remission are the main obstacles to cure in T-cell Acute Lymphoblastic Leukemia (T-ALL). Biomarker guided risk stratification and targeted therapy have the potential to improve outcomes in high-risk T-ALL; however, cellular and genetic factors contributing to treatment resistance remain unknown. Previous bulk genomic studies in T-ALL have implicated tumor heterogeneity as an unexplored mechanism for treatment failure. To link tumor subpopulations with clinical outcome, we created an atlas of healthy pediatric hematopoiesis and applied single-cell multiomic (CITE-seq/snATAC-seq) analysis to a cohort of 40 cases of T-ALL treated on the Children's Oncology Group AALL0434 clinical trial. The cohort was carefully selected to capture the immunophenotypic diversity of T-ALL, with early T-cell precursor (ETP) and Near/Non-ETP subtypes represented, as well as enriched with both relapsed and treatment refractory cases. Integrated analyses of T-ALL blasts and normal T-cell precursors identified a bone-marrow progenitor-like (BMP-like) leukemia sub-population associated with treatment failure and poor overall survival. The single-cell-derived molecular signature of BMP-like blasts predicted poor outcome across multiple subtypes of T-ALL within two independent patient cohorts using bulk RNA-sequencing data from over 1300 patients. We defined the mutational landscape of BMP-like T-ALL, finding that NOTCH1 mutations additively drive T-ALL blasts away from the BMP-like state. We transcriptionally matched BMP-like blasts to early thymic seeding progenitors that have low NR3C1 expression and high stem cell gene expression, corresponding to a corticosteroid and conventional cytotoxic resistant phenotype we observed in ex vivo drug screening. To identify novel targets for BMP-like blasts, we performed in silico and in vitro drug screening against the BMP-like signature and prioritized BMP-like overexpressed cell-surface (CD44, ITGA4, LGALS1) and intracellular proteins (BCL-2, MCL-1, BTK, NF-κB) as candidates for precision targeted therapy. We established patient derived xenograft models of BMP-high and BMP-low leukemias, which revealed vulnerability of BMP-like blasts to apoptosis-inducing agents, TEC-kinase inhibitors, and proteasome inhibitors. Our study establishes the first multi-omic signatures for rapid risk-stratification and targeted treatment of high-risk T-ALL.

RUNX1 is required in granulocyte-monocyte progenitors to attenuate inflammatory cytokine production by neutrophils.

The transcription factor RUNX1 is mutated in familial platelet disorder with associated myeloid malignancy (FPDMM) and in sporadic myelodysplastic syndrome and leukemia. RUNX1 was shown to regulate inflammation in multiple cell types. Here we show that RUNX1 is required in granulocyte-monocyte progenitors (GMPs) to epigenetically repress two inflammatory signaling pathways in neutrophils: Toll-like receptor 4 (TLR4) and type I interferon (IFN) signaling. RUNX1 loss in GMPs augments neutrophils' inflammatory response to the TLR4 ligand lipopolysaccharide through increased expression of the TLR4 coreceptor CD14. RUNX1 binds Cd14 and other genes encoding proteins in the TLR4 and type I IFN signaling pathways whose chromatin accessibility increases when RUNX1 is deleted. Transcription factor footprints for the effectors of type I IFN signaling-the signal transducer and activator of transcription (STAT1::STAT2) and interferon regulatory factors (IRFs)-were enriched in chromatin that gained accessibility in both GMPs and neutrophils when RUNX1 was lost. STAT1::STAT2 and IRF motifs were also enriched in the chromatin of retrotransposons that were derepressed in RUNX1-deficient GMPs and neutrophils. We conclude that a major direct effect of RUNX1 loss in GMPs is the derepression of type I IFN and TLR4 signaling, resulting in a state of fixed maladaptive innate immunity.

RUNX1 is required in granulocyte-monocyte progenitors to attenuate inflammatory cytokine production by neutrophils.

The transcription factor RUNX1 is mutated in familial platelet disorder with associated myeloid malignancies (FPDMM) and in sporadic myelodysplastic syndrome and leukemia. RUNX1 regulates inflammation in multiple cell types. Here we show that RUNX1 is required in granulocyte-monocyte progenitors (GMPs) to restrict the inflammatory response of neutrophils to toll-like receptor 4 (TLR4) signaling. Loss of RUNX1 in GMPs increased the TLR4 coreceptor CD14 on neutrophils, which contributed to neutrophils’ increased inflammatory cytokine production in response to the TLR4 ligand lipopolysaccharide. RUNX1 loss increased the chromatin accessibility of retrotransposons in GMPs and neutrophils and induced a type I interferon signature characterized by enriched footprints for signal transducer and activator of transcription (STAT1::STAT2) and interferon regulatory factors (IRF) in opened chromatin, and increased expression of interferon-stimulated genes. The overproduction of inflammatory cytokines by neutrophils was reversed by inhibitors of type I IFN signaling. We conclude that RUNX1 restrains the chromatin accessibility of retrotransposons in GMPs and neutrophils, and that loss of RUNX1 increases proinflammatory cytokine production by elevating tonic type I interferon signaling.

Single-cell multiomics reveals increased plasticity, resistant populations, and stem-cell-like blasts in KMT2A-rearranged leukemia.

KMT2A-rearranged (KMT2A-r) infant acute lymphoblastic leukemia (ALL) is a devastating malignancy with a dismal outcome, and younger age at diagnosis is associated with increased risk of relapse. To discover age-specific differences and critical drivers that mediate poor outcome in KMT2A-r ALL, we subjected KMT2A-r leukemias and normal hematopoietic cells from patients of different ages to single-cell multiomics analyses. We uncovered the following critical new insights: leukemia cells from patients <6 months have significantly increased lineage plasticity. Steroid response pathways are downregulated in the most immature blasts from younger patients. We identify a hematopoietic stem and progenitor-like (HSPC-like) population in the blood of younger patients that contains leukemic blasts and form an immunosuppressive signaling circuit with cytotoxic lymphocytes. These observations offer a compelling explanation for the ability of leukemias in young patients to evade chemotherapy and immune-mediated control. Our analysis also revealed preexisting lymphomyeloid primed progenitors and myeloid blasts at initial diagnosis of B-ALL. Tracking of leukemic clones in 2 patients whose leukemia underwent a lineage switch documented the evolution of such clones into frank acute myeloid leukemia (AML). These findings provide critical insights into KMT2A-r ALL and have clinical implications for molecularly targeted and immunotherapy approaches. Beyond infant ALL, our study demonstrates the power of single-cell multiomics to detect tumor intrinsic and extrinsic factors affecting rare but critical subpopulations within a malignant population that ultimately determines patient outcome.

Integrative Bulk and Single-Cell Profiling of Premanufacture T-cell Populations Reveals Factors Mediating Long-Term Persistence of CAR T-cell Therapy.

The adoptive transfer of chimeric antigen receptor (CAR) T cells represents a breakthrough in clinical oncology, yet both between- and within-patient differences in autologously derived T cells are a major contributor to therapy failure. To interrogate the molecular determinants of clinical CAR T-cell persistence, we extensively characterized the premanufacture T cells of 71 patients with B-cell malignancies on trial to receive anti-CD19 CAR T-cell therapy. We performed RNA-sequencing analysis on sorted T-cell subsets from all 71 patients, followed by paired Cellular Indexing of Transcriptomes and Epitopes (CITE) sequencing and single-cell assay for transposase-accessible chromatin sequencing (scATAC-seq) on T cells from six of these patients. We found that chronic IFN signaling regulated by IRF7 was associated with poor CAR T-cell persistence across T-cell subsets, and that the TCF7 regulon not only associates with the favorable naïve T-cell state, but is maintained in effector T cells among patients with long-term CAR T-cell persistence. These findings provide key insights into the underlying molecular determinants of clinical CAR T-cell function. SIGNIFICANCE: To improve clinical outcomes for CAR T-cell therapy, there is a need to understand the molecular determinants of CAR T-cell persistence. These data represent the largest clinically annotated molecular atlas in CAR T-cell therapy to date, and significantly advance our understanding of the mechanisms underlying therapeutic efficacy.This article is highlighted in the In This Issue feature, p. 2113.

Developmental trajectory of prehematopoietic stem cell formation from endothelium.

Hematopoietic stem and progenitor cells (HSPCs) in the bone marrow are derived from a small population of hemogenic endothelial (HE) cells located in the major arteries of the mammalian embryo. HE cells undergo an endothelial to hematopoietic cell transition, giving rise to HSPCs that accumulate in intra-arterial clusters (IAC) before colonizing the fetal liver. To examine the cell and molecular transitions between endothelial (E), HE, and IAC cells, and the heterogeneity of HSPCs within IACs, we profiled ∼40 000 cells from the caudal arteries (dorsal aorta, umbilical, vitelline) of 9.5 days post coitus (dpc) to 11.5 dpc mouse embryos by single-cell RNA sequencing and single-cell assay for transposase-accessible chromatin sequencing. We identified a continuous developmental trajectory from E to HE to IAC cells, with identifiable intermediate stages. The intermediate stage most proximal to HE, which we term pre-HE, is characterized by increased accessibility of chromatin enriched for SOX, FOX, GATA, and SMAD motifs. A developmental bottleneck separates pre-HE from HE, with RUNX1 dosage regulating the efficiency of the pre-HE to HE transition. A distal candidate Runx1 enhancer exhibits high chromatin accessibility specifically in pre-HE cells at the bottleneck, but loses accessibility thereafter. Distinct developmental trajectories within IAC cells result in 2 populations of CD45+ HSPCs; an initial wave of lymphomyeloid-biased progenitors, followed by precursors of hematopoietic stem cells (pre-HSCs). This multiomics single-cell atlas significantly expands our understanding of pre-HSC ontogeny.

scATAC-pro: a comprehensive workbench for single-cell chromatin accessibility sequencing data.

Single-cell chromatin accessibility sequencing has become a powerful technology for understanding epigenetic heterogeneity of complex tissues. However, there is a lack of open-source software for comprehensive processing, analysis, and visualization of such data generated using all existing experimental protocols. Here, we present scATAC-pro for quality assessment, analysis, and visualization of single-cell chromatin accessibility sequencing data. scATAC-pro computes a range of quality control metrics for several key steps of experimental protocols, with a flexible choice of methods. It generates summary reports for both quality assessment and downstream analysis. scATAC-pro is available at https://github.com/tanlabcode/scATAC-pro.

Spatial Genome Re-organization between Fetal and Adult Hematopoietic Stem Cells.

Fetal hematopoietic stem cells (HSCs) undergo a developmental switch to become adult HSCs with distinct functional properties. To better understand the molecular mechanisms underlying the developmental switch, we have conducted deep sequencing of the 3D genome, epigenome, and transcriptome of fetal and adult HSCs in mouse. We find that chromosomal compartments and topologically associating domains (TADs) are largely conserved between fetal and adult HSCs. However, there is a global trend of increased compartmentalization and TAD boundary strength in adult HSCs. In contrast, intra-TAD chromatin interactions are much more dynamic and widespread, involving over a thousand gene promoters and distal enhancers. These developmental-stage-specific enhancer-promoter interactions are mediated by different sets of transcription factors, such as TCF3 and MAFB in fetal HSCs, versus NR4A1 and GATA3 in adult HSCs. Loss-of-function studies of TCF3 confirm the role of TCF3 in mediating condition-specific enhancer-promoter interactions and gene regulation in fetal HSCs.

Unified Cox model based multifactor dimensionality reduction method for gene-gene interaction analysis of the survival phenotype.

BACKGROUND: One strategy for addressing missing heritability in genome-wide association study is gene-gene interaction analysis, which, unlike a single gene approach, involves high-dimensionality. The multifactor dimensionality reduction method (MDR) has been widely applied to reduce multi-levels of genotypes into high or low risk groups. The Cox-MDR method has been proposed to detect gene-gene interactions associated with the survival phenotype by using the martingale residuals from a Cox model. However, this method requires a cross-validation procedure to find the best SNP pair among all possible pairs and the permutation procedure should be followed for the significance of gene-gene interactions. Recently, the unified model based multifactor dimensionality reduction method (UM-MDR) has been proposed to unify the significance testing with the MDR algorithm within the regression model framework, in which neither cross-validation nor permutation testing are needed. In this paper, we proposed a simple approach, called Cox UM-MDR, which combines Cox-MDR with the key procedure of UM-MDR to identify gene-gene interactions associated with the survival phenotype. RESULTS: The simulation study was performed to compare Cox UM-MDR with Cox-MDR with and without the marginal effects of SNPs. We found that Cox UM-MDR has similar power to Cox-MDR without marginal effects, whereas it outperforms Cox-MDR with marginal effects and more robust to heavy censoring. We also applied Cox UM-MDR to a dataset of leukemia patients and detected gene-gene interactions with regard to the survival time. CONCLUSION: Cox UM-MDR is easily implemented by combining Cox-MDR with UM-MDR to detect the significant gene-gene interactions associated with the survival time without cross-validation and permutation testing. The simulation results are shown to demonstrate the utility of the proposed method, which achieves at least the same power as Cox-MDR in most scenarios, and outperforms Cox-MDR when some SNPs having only marginal effects might mask the detection of the causal epistasis.

Estimation of Area Under the ROC Curve under nonignorable verification bias.

The Area Under the Receiving Operating Characteristic Curve (AUC) is frequently used for assessing the overall accuracy of a diagnostic marker. However, estimation of AUC relies on knowledge of the true outcomes of subjects: diseased or non-diseased. Because disease verification based on a gold standard is often expensive and/or invasive, only a limited number of patients are sent to verification at doctors' discretion. Estimation of AUC is generally biased if only small verified samples are used and it is thus necessary to make corrections for such lack of information. However, correction based on the ignorable missingness assumption (or missing at random) is also biased if the missing mechanism indeed depends on the unknown disease outcome, which is called nonignorable missing. In this paper, we propose a propensity-score-adjustment method for estimating AUC based on the instrumental variable assumption when the missingness of disease status is nonignorable. The new method makes parametric assumptions on the verification probability, and the probability of being diseased for verified samples rather than for the whole sample. The proposed parametric assumption on the observed sample is easier to be verified than the parametric assumption on the full sample. We establish the asymptotic properties of the proposed estimators. A simulation study is performed to compare the proposed method with existing methods. The proposed method is also applied to an Alzheimer's disease data collected by National Alzheimer's Coordinating Center.

Identifying topologically associating domains and subdomains by Gaussian Mixture model And Proportion test.

The spatial organization of the genome plays a critical role in regulating gene expression. Recent chromatin interaction mapping studies have revealed that topologically associating domains and subdomains are fundamental building blocks of the three-dimensional genome. Identifying such hierarchical structures is a critical step toward understanding the three-dimensional structure-function relationship of the genome. Existing computational algorithms lack statistical assessment of domain predictions and are computationally inefficient for high-resolution Hi-C data. We introduce the Gaussian Mixture model And Proportion test (GMAP) algorithm to address the above-mentioned challenges. Using simulated and experimental Hi-C data, we show that domains identified by GMAP are more consistent with multiple lines of supporting evidence than three state-of-the-art methods. Application of GMAP to normal and cancer cells reveals several unique features of subdomain boundary as compared to domain boundary, including its higher dynamics across cell types and enrichment for somatic mutations in cancer.Spatial organization of the genome plays a crucial role in regulating gene expression. Here the authors introduce GMAP, the Gaussian Mixture model And Proportion test, to identify topologically associating domains and subdomains in Hi-C data.

A unified model based multifactor dimensionality reduction framework for detecting gene-gene interactions.

MOTIVATION: Gene-gene interaction (GGI) is one of the most popular approaches for finding and explaining the missing heritability of common complex traits in genome-wide association studies. The multifactor dimensionality reduction (MDR) method has been widely studied for detecting GGI effects. However, there are several disadvantages of the existing MDR-based approaches, such as the lack of an efficient way of evaluating the significance of multi-locus models and the high computational burden due to intensive permutation. Furthermore, the MDR method does not distinguish marginal effects from pure interaction effects. METHODS: We propose a two-step unified model based MDR approach (UM-MDR), in which, the significance of a multi-locus model, even a high-order model, can be easily obtained through a regression framework with a semi-parametric correction procedure for controlling Type I error rates. In comparison to the conventional permutation approach, the proposed semi-parametric correction procedure avoids heavy computation in order to achieve the significance of a multi-locus model. The proposed UM-MDR approach is flexible in the sense that it is able to incorporate different types of traits and evaluate significances of the existing MDR extensions. RESULTS: The simulation studies and the analysis of a real example are provided to demonstrate the utility of the proposed method. UM-MDR can achieve at least the same power as MDR for most scenarios, and it outperforms MDR especially when there are some single nucleotide polymorphisms that only have marginal effects, which masks the detection of causal epistasis for the existing MDR approaches. CONCLUSIONS: UM-MDR provides a very good supplement of existing MDR method due to its efficiency in achieving significance for every multi-locus model, its power and its flexibility of handling different types of traits. AVAILABILITY AND IMPLEMENTATION: A R package "umMDR" and other source codes are freely available at http://statgen.snu.ac.kr/software/umMDR/ CONTACT: tspark@stats.snu.ac.kr SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Gene-Gene Interaction Analysis for the Accelerated Failure Time Model Using a Unified Model-Based Multifactor Dimensionality Reduction Method.

Although a large number of genetic variants have been identified to be associated with common diseases through genome-wide association studies, there still exits limitations in explaining the missing heritability. One approach to solving this missing heritability problem is to investigate gene-gene interactions, rather than a single-locus approach. For gene-gene interaction analysis, the multifactor dimensionality reduction (MDR) method has been widely applied, since the constructive induction algorithm of MDR efficiently reduces high-order dimensions into one dimension by classifying multi-level genotypes into high- and low-risk groups. The MDR method has been extended to various phenotypes and has been improved to provide a significance test for gene-gene interactions. In this paper, we propose a simple method, called accelerated failure time (AFT) UM-MDR, in which the idea of a unified model-based MDR is extended to the survival phenotype by incorporating AFT-MDR into the classification step. The proposed AFT UM-MDR method is compared with AFT-MDR through simulation studies, and a short discussion is given.

Multivariate Quantitative Multifactor Dimensionality Reduction for Detecting Gene-Gene Interactions.

OBJECTIVES: To determine gene-gene interactions and missing heritability of complex diseases is a challenging topic in genome-wide association studies. The multifactor dimensionality reduction (MDR) method is one of the most commonly used methods for identifying gene-gene interactions with dichotomous phenotypes. For quantitative phenotypes, the generalized MDR or quantitative MDR (QMDR) methods have been proposed. These methods are known as univariate methods because they consider only one phenotype. To date, there are few methods for analyzing multiple phenotypes. METHODS: To address this problem, we propose a multivariate QMDR method (Multi-QMDR) for multivariate correlated phenotypes. We summarize the multivariate phenotypes into a univariate score by dimensional reduction analysis, and then classify the samples accordingly into high-risk and low-risk groups. We use different ways of summarizing mainly based on the principal components. Multi-QMDR is model-free and easy to implement. RESULTS: Multi-QMDR is applied to lipid-related traits. The properties of Multi- QMDR were investigated through simulation studies. Empirical studies show that Multi-QMDR outperforms existing univariate and multivariate methods at identifying causal interactions. CONCLUSIONS: The Multi-QMDR approach improves the performance of QMDR when multiple quantitative phenotypes are available.

The dynamics of NF-κB pathway regulated by circadian clock.

The circadian clock regulates many physiological parameters involving immune response to infectious agents, which is mediated by activation of the transcription factor NF-κB. Thus, understanding the NF-κB dynamics regulated by circadian clocks will help in developing better therapeutics. To this end, we proposed a detailed model in the present work on the basis of understanding inflammatory response under control from circadian clocks. Our results show that the frequencies and amplitudes of the NF-κB oscillation are dependent on the strength and modes of coupling to circadian clock. This circadian control of NF-κB pathway can therefore serve as a useful mechanism in keeping the system in check and controlling inflammatory response induced by infection and other agents. The results are consistent with earlier experimental findings.

Comparison of Paired ROC Curves through a Two-Stage Test.

The area under the receiver operating characteristic (ROC) curve (AUC) is a popularly used index when comparing two ROC curves. Statistical tests based on it for analyzing the difference have been well developed. However, this index is less informative when two ROC curves cross and have similar AUCs. In order to detect differences between ROC curves in such situations, a two-stage nonparametric test that uses a shifted area under the ROC curve (sAUC), along with AUCs, is proposed for paired designs. The new procedure is shown, numerically, to be effective in terms of power under a wide range of scenarios; additionally, it outperforms two conventional ROC-type tests, especially when two ROC curves cross each other and have similar AUCs. Larger sAUC implies larger partial AUC at the range of low false-positive rates in this case. Because high specificity is important in many classification tasks, such as medical diagnosis, this is an appealing characteristic. The test also implicitly analyzes the equality of two commonly used binormal ROC curves at every operating point. We also apply the proposed method to synthesized data and two real examples to illustrate its usefulness in practice.

Bistable switch in let-7 miRNA biogenesis pathway involving Lin28.

miRNAs are small noncoding RNAs capable of regulating gene expression at the post-transcriptional level. A growing body of evidence demonstrated that let-7 family of miRNAs, as one of the highly conserved miRNAs, plays an important role in cell differentiation and development, as well as tumor suppressor function depending on their levels of expression. To explore the physiological significance of let-7 in regulating cell fate decisions, we present a coarse grained model of let-7 biogenesis network, in which let-7 and its regulator Lin28 inhibit mutually. The dynamics of this minimal network architecture indicates that, as the concentration of Lin28 increases, the system undergoes a transition from monostability to a bistability and then to a one-way switch with increasing strength of positive feedback of let-7, while in the absence of Lin28 inhibition, the system loses bistability. Moreover, the ratio of degradation rates of let-7 and Lin28 is critical for the switching sensitivity and resistance to stimulus fluctuations. These findings may highlight why let-7 is required for normal gene expression in the context of embryonic development and oncogenesis, which will facilitate the development of approaches to exploit this regulatory pathway by manipulating Lin28/let-7 axis for novel treatments of human diseases.

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

MOTIVATION: It 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. RESULTS: We 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. CONCLUSION: We 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.