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Boomerang: A method for recursive reclassification.

ABSTRACT: While there are many validated prognostic classifiers used in practice, often their accuracy is modest and heterogeneity in clinical outcomes exists in one or more risk subgroups. Newly available markers, such as genomic mutations, may be used to improve the accuracy of an existing classifier by reclassifying patients from a heterogenous group into a higher or lower risk category. The statistical tools typically applied to develop the initial classifiers are not easily adapted toward this reclassification goal. In this article, we develop a new method designed to refine an existing prognostic classifier by incorporating new markers. The two-stage algorithm called Boomerang first searches for modifications of the existing classifier that increase the overall predictive accuracy and then merges to a prespecified number of risk groups. Resampling techniques are proposed to assess the improvement in predictive accuracy when an independent validation data set is not available. The performance of the algorithm is assessed under various simulation scenarios where the marker frequency, degree of censoring, and total sample size are varied. The results suggest that the method selects few false positive markers and is able to improve the predictive accuracy of the classifier in many settings. Lastly, the method is illustrated on an acute myeloid leukemia data set where a new refined classifier incorporates four new mutations into the existing three category classifier and is validated on an independent data set.

SUBMITTER: Devlin SM

PROVIDER: S-EPMC4940305 | biostudies-literature | 2016 Sep

REPOSITORIES: biostudies-literature

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Boomerang: A method for recursive reclassification.

Devlin Sean M SM Ostrovnaya Irina I Gönen Mithat M

Biometrics 20160111 3

While there are many validated prognostic classifiers used in practice, often their accuracy is modest and heterogeneity in clinical outcomes exists in one or more risk subgroups. Newly available markers, such as genomic mutations, may be used to improve the accuracy of an existing classifier by reclassifying patients from a heterogenous group into a higher or lower risk category. The statistical tools typically applied to develop the initial classifiers are not easily adapted toward this reclas ...[more]

PMID: 26754051

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Project description:Interactions between genetic and/or environmental factors are ubiquitous, affecting the phenotypes of organisms in complex ways. Knowledge about such interactions is becoming rate-limiting for our understanding of human disease and other biological phenomena. Phenomics refers to the integrative analysis of how all genes contribute to phenotype variation, entailing genome and organism level information. A systems biology view of gene interactions is critical for phenomics. Unfortunately the problem is intractable in humans; however, it can be addressed in simpler genetic model systems. Our research group has focused on the concept of genetic buffering of phenotypic variation, in studies employing the single-cell eukaryotic organism, S. cerevisiae. We have developed a methodology, quantitative high throughput cellular phenotyping (Q-HTCP), for high-resolution measurements of gene-gene and gene-environment interactions on a genome-wide scale. Q-HTCP is being applied to the complete set of S. cerevisiae gene deletion strains, a unique resource for systematically mapping gene interactions. Genetic buffering is the idea that comprehensive and quantitative knowledge about how genes interact with respect to phenotypes will lead to an appreciation of how genes and pathways are functionally connected at a systems level to maintain homeostasis. However, extracting biologically useful information from Q-HTCP data is challenging, due to the multidimensional and nonlinear nature of gene interactions, together with a relative lack of prior biological information. Here we describe a new approach for mining quantitative genetic interaction data called recursive expectation-maximization clustering (REMc). We developed REMc to help discover phenomic modules, defined as sets of genes with similar patterns of interaction across a series of genetic or environmental perturbations. Such modules are reflective of buffering mechanisms, i.e., genes that play a related role in the maintenance of physiological homeostasis. To develop the method, 297 gene deletion strains were selected based on gene-drug interactions with hydroxyurea, an inhibitor of ribonucleotide reductase enzyme activity, which is critical for DNA synthesis. To partition the gene functions, these 297 deletion strains were challenged with growth inhibitory drugs known to target different genes and cellular pathways. Q-HTCP-derived growth curves were used to quantify all gene interactions, and the data were used to test the performance of REMc. Fundamental advantages of REMc include objective assessment of total number of clusters and assignment to each cluster a log-likelihood value, which can be considered an indicator of statistical quality of clusters. To assess the biological quality of clusters, we developed a method called gene ontology information divergence z-score (GOid_z). GOid_z summarizes total enrichment of GO attributes within individual clusters. Using these and other criteria, we compared the performance of REMc to hierarchical and K-means clustering. The main conclusion is that REMc provides distinct efficiencies for mining Q-HTCP data. It facilitates identification of phenomic modules, which contribute to buffering mechanisms that underlie cellular homeostasis and the regulation of phenotypic expression.

Project description:BackgroundBreast reconstruction using the latissimus dorsi (LD) flap is one of the most popular and common breast reconstruction techniques among Asian patients. There are increasing numbers of cases wherein breasts need to be reconstructed to moderate to large sizes among the Asian population. Most reconstructive surgeons use abdominal-based flaps-such as the deep inferior epigastric perforator and transverse rectus abdominis myocutaneous flap, or LD flap with an implant-to supplement the volume. Here, we compare the usefulness of the boomerang LD (bLD) flap-a technique developed by modifying the design of the conventional extended LD flap-with that of the LD flap with implant (LDi).MethodsThis was a prospective cohort study including patients with breast cancer aged between 25 and 60 years who underwent unilateral total or skin/nipple-sparing mastectomy, or postmastectomy. The exclusion criteria were advanced breast cancer (stages 3 and 4); a history of cognitive impairment affecting ability to complete the self-reported questionnaire; a history of neurologic or musculoskeletal disorder; and a history of alcohol or drug abuse. Statistical analysis was performed, and correlations between the two technique types were analyzed [including age, body mass index, preoperative breast volume, operation time, flap elevation time, admission duration, adjuvant treatment (radiotherapy, chemotherapy), drain indwelling duration, and drain total sum volume].ResultsIn total, 85 patients who underwent immediate breast construction through the LDi group (n=63) or bLD group (n=25) techniques after total mastectomy between January 2015 and June 2022 were analyzed (mean age: LDi group, 46±7.7 years; bLD group, 45.6±7.8 years). We observed that the flap weight, operative time, mean admission duration, and drain indwelling duration were statistically significantly different in the bLD group (P<0.05). Body mass index, preoperative breast volume, specimen weight, flap elevation time, and drain total time were comparable between groups.ConclusionsBreast reconstruction using the bLD flap was found to be a suitable alternative technique for patients who desire an autologous tissue transfer (rather than an implant) but are contraindicated for abdominal-based flaps and do not mind long postoperative scars.

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Boomerang: A method for recursive reclassification.

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Boomerang: A method for recursive reclassification.

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