James D. Malley

New genes involved in cancer identified by retroviral tagging

Retroviral insertional mutagenesis in BXH2 and AKXD mice induces a high incidence of myeloid leukemia and B- and T-cell lymphoma, respectively. The retroviral integration sites (RISs) in these tumors thus provide powerful genetic tags for the discovery of genes involved in cancer. Here we report the first large-scale use of retroviral tagging for cancer gene discovery in the post-genome era. Using high throughput inverse PCR, we cloned and analyzed the sequences of 884 RISs from a tumor panel composed primarily of B-cell lymphomas. We then compared these sequences, and another 415 RIS sequences previously cloned from BXH2 myeloid leukemias and from a few AKXD lymphomas, against the recently assembled mouse genome sequence. These studies identified 152 loci that are targets of retroviral integration in more than one tumor (common retroviral integration sites, CISs) and therefore likely to encode a cancer gene. Thirty-six CISs encode genes that are known or predicted to be genes involved in human cancer or their homologs, whereas others encode candidate genes that have not yet been examined for a role in human cancer. Our studies demonstrate the power of retroviral tagging for cancer gene discovery in the post-genome era and indicate a largely unrecognized complexity in mouse and presumably human cancer.

A survey of ovary-, testis-, and soma-biased gene expression in Drosophila melanogaster adults

BackgroundSexual dimorphism results in the formation of two types of individuals with specialized reproductive roles and is most evident in the germ cells and gonads.ResultsWe have undertaken a global analysis of transcription between the sexes using a 31,464 element FlyGEM microarray to determine what fraction of the genome shows sex-biased expression, what tissues express these genes, the predicted functions of these genes, and where these genes map onto the genome. Females and males (both with and without gonads), dissected testis and ovary, females and males with genetically ablated germlines, and sex-transformed flies were sampled.ConclusionsUsing any of a number of criteria, we find extensive sex-biased expression in adults. The majority of cases of sex differential gene expression are attributable to the germ cells. There is also a large class of genes with soma-biased expression. There is little germline-biased expression indicating that nearly all genes with germline expression also show sex-bias. Monte Carlo simulations show that some genes with sex-biased expression are non-randomly distributed in the genome.

Immunogenetic risk and protective factors for the idiopathic inflammatory myopathies: distinct HLA-A, -B, -Cw, -DRB1, and -DQA1 allelic profiles distinguish European American patients with different myositis autoantibodies.

Abstract: The idiopathic inflammatory myopathies (IIM) are systemic connective tissue diseases defined by chronic muscle inflammation and weakness associated with autoimmunity. We have performed low to high resolution molecular typing to assess the genetic variability of major histocompatibility complex loci (HLA-A, -B, -Cw, -DRB1, and -DQA1) in a large population of European American patients with IIM (n = 571) representing the major myositis autoantibody groups. We established that alleles of the 8.1 ancestral haplotype (8.1 AH) are important risk factors for the development of IIM in patients producing anti-synthetase/anti-Jo-1, -La, -PM/Scl, and -Ro autoantibodies. Moreover, a random forests classification analysis suggested that 8.1 AH-associated alleles B*0801 and DRB1*0301 are the principal HLA risk markers. In addition, we have identified several novel HLA susceptibility factors associated distinctively with particular myositis-specific (MSA) and myositis-associated autoantibody (MAA) groups of the IIM. IIM patients with anti-PL-7 (anti-threonyl-tRNA synthetase) autoantibodies have a unique HLA Class I risk allele, Cw*0304 (pcorr = 0.046), and lack the 8.1 AH markers associated with other anti-synthetase autoantibodies (for example, anti-Jo-1 and anti-PL-12). In addition, HLA-B*5001 and DQA1*0104 are novel potential risk factors among anti-signal recognition particle autoantibody-positive IIM patients (pcorr = 0.024 and p = 0.010, respectively). Among those patients with MAA, HLA DRB1*11 and DQA1*06 alleles were identified as risk factors for myositis patients with anti-Ku (pcorr = 0.041) and anti-La (pcorr = 0.023) autoantibodies, respectively. Amino acid sequence analysis of the HLA DRB1 third hypervariable region identified a consensus motif, 70D (hydrophilic)/71R (basic)/74A (hydrophobic), conferring protection among patients producing anti-synthetase/anti-Jo-1 and -PM/Scl autoantibodies. Together, these data demonstrate that HLA signatures, comprising both risk and protective alleles or motifs, distinguish IIM patients with different myositis autoantibodies and may have diagnostic and pathogenic implications. Variations in associated polymorphisms for these immune response genes may reflect divergent pathogenic mechanisms and/or responses to unique environmental triggers in different groups of subjects resulting in the heterogeneous syndromes of the IIM. Abbreviations: AH = ancestral haplotype, DM = dermatomyositis, EA = European Americans, HVR3 = third hypervariable region, IBM = inclusion body myositis, IIM = idiopathic inflammatory myopathies, MAA = myositis-associated autoantibodies, MHC = major histocompatibility complex, MSA = myositis-specific autoantibodies, PM = polymyositis, RF = random forests, RSP = restrictive supertype patterns, SRP = signal recognition particle.

Clinical and immunogenetic prognostic factors for radiographic severity in ankylosing spondylitis

OBJECTIVE To improve prognostic ability in ankylosing spondylitis (AS), we sought to identify demographic, clinical, and immunogenetic characteristics associated with radiographic severity in a large cohort of patients. METHODS Patients with AS for > or =20 years were enrolled in a cross-sectional study (n = 398). Pelvic and spinal radiographs were scored using the Bath Ankylosing Spondylitis Radiology Index for the spine (BASRI-s), and radiographic severity was measured as the BASRI-s/duration of AS. Clinical factors and HLA-B, DR, DQ, and DP alleles associated with the highest quartile of the distribution of radiographic severity were identified by first using random forests and then using multivariable logistic regression modeling. Similar procedures were used to identify factors associated with the lowest quartile of radiographic severity. RESULTS Radiographic severity (being in the top quartile of BASRI-s/duration of AS) was associated with older age at onset of AS (odds ratio [OR] 1.10 per year), male sex (OR 1.90), current smoker (OR 4.72), and the presence of HLA-B*4100 (OR 11.73), DRB1*0804 (OR 12.32), DQA1*0401 (OR 5.24), DQB1*0603 (OR 3.42), and DPB1*0202 (OR 23.36), whereas the presence of DRB1*0801 was strongly negatively associated (OR 0.03). Being in the lowest quartile of BASRI-s/duration of AS was also less likely among those with an older age at onset of AS (OR 0.94 per year), men (OR 0.28), and current smokers (OR 0.29). CONCLUSION The accuracy of the prognosis of radiographic severity in AS is improved by knowing the age at disease onset, sex, smoking history, and the presence of HLA-B*4100, DRB1*0804, DQA1*0401, DQB1*0603, DRB1*0801, and DPB1*0202 alleles.

Computer‐assisted detection of colonic polyps with CT colonography using neural networks and binary classification trees

Detection of colonic polyps in CT colonography is problematic due to complexities of polyp shape and the surface of the normal colon. Published results indicate the feasibility of computer-aided detection of polyps but better classifiers are needed to improve specificity. In this paper we compare the classification results of two approaches: neural networks and recursive binary trees. As our starting point we collect surface geometry information from three-dimensional reconstruction of the colon, followed by a filter based on selected variables such as region density, Gaussian and average curvature and sphericity. The filter returns sites that are candidate polyps, based on earlier work using detection thresholds, to which the neural nets or the binary trees are applied. A data set of 39 polyps from 3 to 25 mm in size was used in our investigation. For both neural net and binary trees we use tenfold cross-validation to better estimate the true error rates. The backpropagation neural net with one hidden layer trained with Levenberg-Marquardt algorithm achieved the best results: sensitivity 90% and specificity 95% with 16 false positives per study.

Multiple neural network classification scheme for detection of colonic polyps in CT colonography data sets.

RATIONALE AND OBJECTIVES A new classification system for colonic polyp detection, designed to increase sensitivity and reduce the number of false-positive findings with computed tomographic colonography, was developed and tested in this study. MATERIALS AND METHODS The system involves classification by a committee of neural networks (NNs), each using largely distinct subsets of features selected from a general set. Back-propagation NNs trained with the Levenberg-Marquardt algorithm were used as primary classifiers (committee members). The set of features included region density, Gaussian and mean curvature and sphericity, lesion size, colon wall thickness, and the means and standard deviations of all of these values. Subsets of variables were initially selected because of their effectiveness according to training and test sample misclassification rates. The final decision for each case is based on the majority vote across the networks and reflects the weighted votes of all networks. The authors also introduce a smoothed cross-validation method designed to improve estimation of the true misclassification rates by reducing bias and variance. RESULTS This committee method reduced the false-positive rate by 36%, a clinically meaningful reduction, and improved sensitivity by an average of 6.9% compared with decisions made by any single NN. The overall sensitivity and specificity were 82.9% and 95.3%, respectively, when sensitivity was estimated by means of smoothed cross-validation. CONCLUSION The proposed method of using multiple classifiers and majority voting is recommended for classification tasks with large sets of input features, particularly when selected feature subsets may not be equally effective and do not provide satisfactory true- and false-positive rates. This approach reduces variance in estimates of misclassification rates.

Immunogenetic Risk and Protective Factors for the Idiopathic Inflammatory Myopathies: Distinct Hla-a, -b, -cw, -drb1 and -dqa1 Allelic Profiles and Motifs Define Clinicopathologic Groups in Caucasians

Abstract: The idiopathic inflammatory myopathies (IIM) are systemic connective tissue diseases in which autoimmune pathology is suspected to promote chronic muscle inflammation and weakness. We have performed low to high resolution genotyping to characterize the allelic profiles of HLA-A, -B, -Cw, -DRB1, and -DQA1 loci in a large population of North American Caucasian patients with IIM representing the major clinicopathologic groups (n = 571). We confirmed that alleles of the 8.1 ancestral haplotype were important risk markers for the development of IIM, and a random forests classification analysis suggested that within this haplotype, HLA-B*0801, DRB1*0301 and/ or closely linked genes are the principal HLA risk factors. In addition, we identified several novel HLA factors associated distinctly with 1 or more clinicopathologic groups of IIM. The DQA1*0201 allele and associated peptide-binding motif (47KLPLFHRL54) were exclusive protective factors for the CD8+ T cell-mediated IIM forms of polymyositis (PM) and inclusion body myositis (IBM) (pc < 0.005). In contrast, HLA-A*68 alleles were significant risk factors for dermatomyositis (DM) (pc = 0.0021), a distinct clinical group thought to involve a humorally mediated immunopathology. While the DQA1*0301 allele was detected as a possible risk factor for IIM, PM, and DM patients (p < 0.05), DQA1*03 alleles were protective factors for IBM (pc = 0.0002). Myositis associated with malignancies was the most distinctive group of IIM wherein HLA Class I alleles were the only identifiable susceptibility factors and a shared HLA-Cw peptide-binding motif (90AGSHTLQWM98) conferred significant risk (pc = 0.019). Together, these data suggest that HLA susceptibility markers distinguish different myositis phenotypes with divergent pathogenetic mechanisms. These variations in associated HLA polymorphisms may reflect responses to unique environmental triggers resulting in the tissue pathospecificity and distinct clinicopathologic syndromes of the IIM. Abbreviations: AH = ancestral haplotype, CAM = cancer-associated myositis, CTM = connective tissue disease overlap myositis, DM = dermatomyositis, IBM = inclusion body myositis, IIM = idiopathic inflammatory myopathies, MHC = major histocompatibility complex, PM = polymyositis, RF = random forests, RSP = restrictive supertype patterns.

The Clinical Phenotypes of the Juvenile Idiopathic Inflammatory Myopathies

AbstractThe juvenile idiopathic inflammatory myopathies (JIIM) are systemic autoimmune diseases characterized by skeletal muscle weakness, characteristic rashes, and other systemic features. Although juvenile dermatomyositis (JDM), the most common form of JIIM, has been well studied, the other major clinical subgroups of JIIM, including juvenile polymyositis (JPM) and juvenile myositis overlapping with another autoimmune or connective tissue disease (JCTM), have not been well characterized, and their similarity to the adult clinical subgroups is unknown. We enrolled 436 patients with JIIM, including 354 classified as JDM, 33 as JPM, and 49 as JCTM, in a nationwide registry study. The aim of the study was to compare demographics; clinical features; laboratory measures, including myositis autoantibodies; and outcomes among these clinical subgroups, as well as with published data on adult patients with idiopathic inflammatory myopathies (IIM) enrolled in a separate natural history study.We used random forest classification and logistic regression modeling to compare clinical subgroups, following univariate analysis. JDM was characterized by typical rashes, including Gottron papules, heliotrope rash, malar rash, periungual capillary changes, and other photosensitive and vasculopathic skin rashes. JPM was characterized by more severe weakness, higher creatine kinase levels, falling episodes, and more frequent cardiac disease. JCTM had more frequent interstitial lung disease, Raynaud phenomenon, arthralgia, and malar rash. Differences in autoantibody frequency were also evident, with anti-p155/140, anti-MJ, and anti-Mi-2 seen more frequently in patients with JDM, anti-signal recognition particle and anti-Jo-1 in JPM, and anti-U1-RNP, PM-Scl, and other myositis-associated autoantibodies more commonly present in JCTM. Mortality was highest in patients with JCTM, whereas hospitalizations and wheelchair use were highest in JPM patients. Several demographic and clinical features were shared between juvenile and adult IIM subgroups. However, JDM and JPM patients had a lower frequency of interstitial lung disease, Raynaud phenomenon, “mechanic’s hands” and carpal tunnel syndrome, and lower mortality than their adult counterparts. We conclude that juvenile myositis is a heterogeneous group of illnesses with distinct clinical subgroups, defined by varying clinical and demographic characteristics, laboratory features, and outcomes.

Probability machines: consistent probability estimation using nonparametric learning machines.

BACKGROUND Most machine learning approaches only provide a classification for binary responses. However, probabilities are required for risk estimation using individual patient characteristics. It has been shown recently that every statistical learning machine known to be consistent for a nonparametric regression problem is a probability machine that is provably consistent for this estimation problem. OBJECTIVES The aim of this paper is to show how random forests and nearest neighbors can be used for consistent estimation of individual probabilities. METHODS Two random forest algorithms and two nearest neighbor algorithms are described in detail for estimation of individual probabilities. We discuss the consistency of random forests, nearest neighbors and other learning machines in detail. We conduct a simulation study to illustrate the validity of the methods. We exemplify the algorithms by analyzing two well-known data sets on the diagnosis of appendicitis and the diagnosis of diabetes in Pima Indians. RESULTS Simulations demonstrate the validity of the method. With the real data application, we show the accuracy and practicality of this approach. We provide sample code from R packages in which the probability estimation is already available. This means that all calculations can be performed using existing software. CONCLUSIONS Random forest algorithms as well as nearest neighbor approaches are valid machine learning methods for estimating individual probabilities for binary responses. Freely available implementations are available in R and may be used for applications.

Evolution and structure of the fibrinogen genes: Random insertion of introns or selective loss?

Chromosomal linkage as well as sequence homologies provide unequivocal evidence that the genes for the alpha, beta and gamma chains of fibrinogen arose by successive duplication of a single ancestral gene. Yet, when the three fibrinogen chains are aligned by amino acid homology, the positions of intervening sequences coincide at only two positions for all three chains. While one additional intron occurs at a homologous site in the beta and gamma chains, none of the positions of the remaining 11 introns in the three genes is shared. This arrangement of introns in the three fibrinogen genes suggests that either introns were selectively lost, implying that there is essential information in the retained introns, or the common introns were present in the ancestral fibrinogen gene and introns have been randomly inserted since the triplication of the original gene. The more likely possibility of selective loss of introns implies that the ancestral gene, as it existed about one billion years ago, must have been composed of numerous small exons.