Shed urinary ALCAM is an independent prognostic biomarker of three-year overall survival after cystectomy in patients with bladder cancer.

Proteins involved in tumor cell migration can potentially serve as markers of invasive disease. Activated Leukocyte Cell Adhesion Molecule (ALCAM) promotes adhesion, while shedding of its extracellular domain is associated with migration. We hypothesized that shed ALCAM in biofluids could be predictive of progressive disease. ALCAM expression in tumor (n = 198) and shedding in biofluids (n = 120) were measured in two separate VUMC bladder cancer cystectomy cohorts by immunofluorescence and enzyme-linked immunosorbent assay, respectively. The primary outcome measure was accuracy of predicting 3-year overall survival (OS) with shed ALCAM compared to standard clinical indicators alone, assessed by multivariable Cox regression and concordance-indices. Validation was performed by internal bootstrap, a cohort from a second institution (n = 64), and treatment of missing data with multiple-imputation. While ALCAM mRNA expression was unchanged, histological detection of ALCAM decreased with increasing stage (P = 0.004). Importantly, urine ALCAM was elevated 17.0-fold (P < 0.0001) above non-cancer controls, correlated positively with tumor stage (P = 0.018), was an independent predictor of OS after adjusting for age, tumor stage, lymph-node status, and hematuria (HR, 1.46; 95% CI, 1.03-2.06; P = 0.002), and improved prediction of OS by 3.3% (concordance-index, 78.5% vs. 75.2%). Urine ALCAM remained an independent predictor of OS after accounting for treatment with Bacillus Calmette-Guerin, carcinoma in situ, lymph-node dissection, lymphovascular invasion, urine creatinine, and adjuvant chemotherapy (HR, 1.10; 95% CI, 1.02-1.19; P = 0.011). In conclusion, shed ALCAM may be a novel prognostic biomarker in bladder cancer, although prospective validation studies are warranted. These findings demonstrate that markers reporting on cell motility can act as prognostic indicators

Boosting the concordance index for survival data - a unified framework to derive and evaluate biomarker combinations

The development of molecular signatures for the prediction of time-to-event outcomes is a methodologically challenging task in bioinformatics and biostatistics. Although there are numerous approaches for the derivation of marker combinations and their evaluation, the underlying methodology often suffers from the problem that different optimization criteria are mixed during the feature selection, estimation and evaluation steps. This might result in marker combinations that are only suboptimal regarding the evaluation criterion of interest. To address this issue, we propose a unified framework to derive and evaluate biomarker combinations. Our approach is based on the concordance index for time-to-event data, which is a non-parametric measure to quantify the discrimatory power of a prediction rule. Specifically, we propose a component-wise boosting algorithm that results in linear biomarker combinations that are optimal with respect to a smoothed version of the concordance index. We investigate the performance of our algorithm in a large-scale simulation study and in two molecular data sets for the prediction of survival in breast cancer patients. Our numerical results show that the new approach is not only methodologically sound but can also lead to a higher discriminatory power than traditional approaches for the derivation of gene signatures.Comment: revised manuscript - added simulation study, additional result

Novel personalized pathway-based metabolomics models reveal key metabolic pathways for breast cancer diagnosis

Comparison of logistic regression, SVM and random forest performance in the plasma training data set. Table S2. Pathway significance and relative log fold changes in our metabolomics data and TCGA breast cancer RNA-Seq data. Table S3. Detected metabolites and their differential test results among the two models. a All-stage diagnosis model. b Early-stage diagnosis model. Table S4. Single-variate logistic analysis of metabolites or pathways selected as features in the metabolite-based or pathway-based early-stage diagnosis model. Table S5. Comparison of pathway features in the full-size (101 input pathways) and half-size (51 input pathways) pathway-based early-stage diagnosis models. (DOCX 34 kb

Breast cancer natural history models and risk prediction in mammography screening cohorts

In this thesis, the foundations are laid for a new natural history model for breast cancer—specifically designed to take advantage of detailed screening cohorts. Three diverse applications of this model are then presented. Study I develops the statistical framework for the natural history model, and shows with simulations that the model parameters can be estimated based on only the information available at diagnosis. It also describes how to adjust for random left truncation—an important aspect to consider when studying prospective cohorts. In Study II, the newly developed natural history model is applied to a Swedish mammography screening cohort. It estimates the population-level distributions of age at onset and tumor volume doubling time. As an extension, the tumor volume doubling time is allowed to depend on the age at onset. The study also estimates the rate of symptomatic detection and screening sensitivity as functions of tumor size. Simulations are used to validate the estimates. Study III shifts the focus from inference to risk prediction. The natural history model is modified to incorporate risk factors separately in each of the four components of the model. Short-term risk prediction is then performed on the screening cohort and the results are compared to a conventional approach to breast cancer risk prediction. The study also develops novel predictions based on, for example, having experienced tumor onset, having a tumor detected at the next screening, and having a tumor detected before it reaches a certain size if attending the next screening. In Study IV, the model is used to study the effect that certain acquisition parameters used in mammography have on the detectability of the breast cancer tumor. With the model, it is possible to more directly study the mammography screening sensitivity, compared to the ad hoc definition of sensitivity commonly seen in the screening literature. It was identified that the compressed breast thickness—in addition to the percent mammographic density and latent tumor size—was inversely associated with the screening sensitivity

Medical imaging analysis with artificial neural networks

Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging

Application of the Time-Dependent ROC Curves for Prognostic Accuracy with Multiple Biomarkers

The rapid advancement in molecule technology has lead to the discovery of many markers that have potential applications in disease diagnosis and prognosis. In a prospective cohort study, information on a panel of biomarkers as well as the disease status for a patient are routinely collected over time. Such information is useful to predict patients\u27 prognosis and select patients for targeted therapy. In this paper, we develop procedures for constructing a composite test with optimal discrimination power when there are multiple markers available to assist in prediction and characterize the accuracy of the resulting test by extending the time-dependent receiver operating characteristic(ROC) curve methodology (Heagerty, Lumley and Pepe, 2000). We employ a modified logistic regression model to derive optimal linear composite scores such that their corresponding ROC curves are maximized at every false positive rate. We provide theoretical justification for using such a model for prognostic accuracy. The proposed method allows for time-varying marker effects and accommodates censored failure time outcome. When the effect of markers are approximately constant over time, we propose more efficient estimating procedures under such model. We conduct numerical studies to evaluate the performance of the proposed procedures. Our results indicate the proposed methods are both flexible and efficient. We contrast these methods with an application to real data concerning the prognostic accuracies of expression levels of 6 genes

Pathway-Based Multi-Omics Data Integration for Breast Cancer Diagnosis and Prognosis.

Ph.D. Thesis. University of Hawaiʻi at Mānoa 2017