Predictive oncology: a review of multidisciplinary, multiscale in silico modeling linking phenotype, morphology and growth.

Empirical evidence and theoretical studies suggest that the phenotype, i.e., cellular- and molecular-scale dynamics, including proliferation rate and adhesiveness due to microenvironmental factors and gene expression that govern tumor growth and invasiveness, also determine gross tumor-scale morphology. It has been difficult to quantify the relative effect of these links on disease progression and prognosis using conventional clinical and experimental methods and observables. As a result, successful individualized treatment of highly malignant and invasive cancers, such as glioblastoma, via surgical resection and chemotherapy cannot be offered and outcomes are generally poor. What is needed is a deterministic, quantifiable method to enable understanding of the connections between phenotype and tumor morphology. Here, we critically assess advantages and disadvantages of recent computational modeling efforts (e.g., continuum, discrete, and cellular automata models) that have pursued this understanding. Based on this assessment, we review a multiscale, i.e., from the molecular to the gross tumor scale, mathematical and computational "first-principle" approach based on mass conservation and other physical laws, such as employed in reaction-diffusion systems. Model variables describe known characteristics of tumor behavior, and parameters and functional relationships across scales are informed from in vitro, in vivo and ex vivo biology. We review the feasibility of this methodology that, once coupled to tumor imaging and tumor biopsy or cell culture data, should enable prediction of tumor growth and therapy outcome through quantification of the relation between the underlying dynamics and morphological characteristics. In particular, morphologic stability analysis of this mathematical model reveals that tumor cell patterning at the tumor-host interface is regulated by cell proliferation, adhesion and other phenotypic characteristics: histopathology information of tumor boundary can be inputted to the mathematical model and used as a phenotype-diagnostic tool to predict collective and individual tumor cell invasion of surrounding tissue. This approach further provides a means to deterministically test effects of novel and hypothetical therapy strategies on tumor behavior.

Modular organization of the cortical connections linking visual areas V1, V2 and V4 in Macaque monkeys

The macaque cortical visual system is hierarchically organized into two streams, the ventral stream for recognizing objects and the dorsal stream for analyzing spatial relationships. The ventral stream extends from striate cortex or area V1 to inferior temporal cortex (IT) through extra-striate areas V2 and V4. Between V1 and V2, the ventral stream consists of two roughly parallel sub-streams, one extending from the cytochrome oxidase (CO) rich blobs in V1 to the CO rich thin stripes in V2, the other extending from the interblobs in V1 to interstripes, in V2. The blob-dominated sub-stream is thought to analyze the surface features such as color, whereas the interblob-dominated one is thought to analyze the contour features such as shape. ^ In the current study, the organization of cortical pathways linking V2 thin stripe and interstripe compartments with area V4 was investigated using a combination of physiological and anatomical techniques. Different compartments of V2 were first characterized, in vivo, using optical recording of intrinsic cortical signals. These functionally derived maps of V2 stripe compartments were then used to guide iontophoretic injections of multiple, distinguishable, anterograde tracers into specific V2 compartments. The distribution of labeled axons was analyzed either in horizontal sections through the prelunate gyrus, or in tangentially sectioned portions of physically unfolded cortex containing the lunate sulcus, prelunate gyrus and superior temporal sulcus. When a V2 thin stripe and adjacent interstripe were injected with distinguishable tracers, a large primary and several secondary foci were observed in V4. The primary focus from the thin stripe injection was spatially segregated from the primary focus from the V2 interstripe injection, suggesting a retention of the pattern of compartmentation. ^ We examined the distribution of retrogradely labeled cells in V1 following the injections of tracers into V2 different compartments, in order to quantitate just how parallel the two sub-streams are from V1 to V2. Our results suggest that both blobs and interblobs project to thin stripes in V2, whereas only interblobs project to interstripes. This asymmetrical segregation argues against the original proposal of strict parallelism. (Abstract shortened by UMI.) ^

Risk Index Score (RISc)

The main objective of this study was to develop and validate a computer-based statistical algorithm based on a multivariable logistic model that can be translated into a simple scoring system in order to ascertain stroke cases using hospital admission medical records data. This algorithm, the Risk Index Score (RISc), was developed using data collected prospectively by the Brain Attack Surveillance in Corpus Christ (BASIC) project. The validity of the RISc was evaluated by estimating the concordance of scoring system stroke ascertainment to stroke ascertainment accomplished by physician review of hospital admission records. The goal of this study was to develop a rapid, simple, efficient, and accurate method to ascertain the incidence of stroke from routine hospital admission hospital admission records for epidemiologic investigations. ^ The main objectives of this study were to develop and validate a computer-based statistical algorithm based on a multivariable logistic model that could be translated into a simple scoring system to ascertain stroke cases using hospital admission medical records data. (Abstract shortened by UMI.)^

Pathways linking socioeconomic position and diabetes: The role of psychosocial factors

Despite increasing interest in the relationship between socioeconomic position (SEP) and health, there remains little understanding of the mechanisms through which SEP is related to chronic disease. This dissertation utilized data from 2,592 U.S. households in the 1995 telephone survey of the Aging, Status, and the Sense of Control study to: (1) investigate potential mediating factors in the association between educational level and prevalence of diabetes and (2) to investigate the association between the three major measures of SEP—income, education, and occupation—and the prevalence of diabetes. Regression analyses were conducted to examine the degree to which sense of personal control and social support mediate the association between level of educational attainment and diabetes and to examine the contribution of each of the SEP measures to diabetes. After adjusting for age, obesity, sex, and race, respondents with less than a high school education had greater odds of having diabetes than those with a college degree or higher level of educational attainment, although the corresponding confidence interval contained the null value (OR = 1.2, 95% CI: 0.7, 2.0). Neither sense of control nor social support significantly mediated the association between education and diabetes. However, sense of control was associated with diabetes status (OR = 0.7, 95% CI: 0.5, 1.0). Compared with income and education, employment status was the most strongly associated measure of SEP with diabetes prevalence. After adjusting for age, obesity, sex, and race, respondents who were unable to work due to disability had fourfold greater odds of having diabetes than those who were employed full time (OR = 4.0; 95% CI: 1.9, 8.3). Adding income and/or education to the model did not improve the fit. Understanding the impact of socioeconomic factors on diabetes requires consideration of multiple measures of SEP as well as the psychosocial pathways through which SEP may influence diabetes. ^

Systematic review of genetic risk score in coronary heart disease and other diseases

In order to better take advantage of the abundant results from large-scale genomic association studies, investigators are turning to a genetic risk score (GRS) method in order to combine the information from common modest-effect risk alleles into an efficient risk assessment statistic. The statistical properties of these GRSs are poorly understood. As a first step toward a better understanding of GRSs, a systematic analysis of recent investigations using a GRS was undertaken. GRS studies were searched in the areas of coronary heart disease (CHD), cancer, and other common diseases using bibliographic databases and by hand-searching reference lists and journals. Twenty-one independent case-control studies, cohort studies, and simulation studies (12 in CHD, 9 in other diseases) were identified. The underlying statistical assumptions of the GRS using the experience of the Framingham risk score were investigated. Improvements in the construction of a GRS guided by the concept of composite indicators are discussed. The GRS will be a promising risk assessment tool to improve prediction and diagnosis of common diseases.^