Long-term survival after multidisciplinary management of resected pancreatic adenocarcinoma.

INTRODUCTION: Actual 5-year survival rates of 10-18% have been reported for patients with resected pancreatic adenocarcinoma (PC), but the use of multimodality therapy was uncommon in these series. We evaluated long-term survival and patterns of recurrence in patients treated for PC with contemporary staging and multimodality therapy. METHODS: We analyzed 329 consecutive patients with PC evaluated between 1990 and 2002 who underwent resection. Each received a multidisciplinary evaluation and a standard operative approach. Pre- or postoperative chemotherapy and/or chemoradiation were routine. Surgical specimens of 5-year survivors were re-reviewed. A multivariate model of factors associated with long-term survival was constructed. RESULTS: Patients underwent pancreaticoduodenectomy (n = 302; 92%), distal (n = 20; 6%), or total pancreatectomy (n = 7; 2%). A total of 108 patients (33%) underwent vascular reconstruction, 301 patients (91%) received neoadjuvant or adjuvant therapy, 157 specimens (48%) were node positive, and margins were microscopically positive in 52 patients (16%). Median overall survival and disease-specific survival was 23.9 and 26.5 months. Eighty-eight patients (27%) survived a minimum of 5 years and had a median overall survival of 11 years. Of these, 21 (24%) experienced recurrence, 7 (8%) after 5 years. Late recurrences occurred most frequently in the lungs, the latest at 6.7 years. Multivariate analysis identified disease-negative lymph nodes (P = .02) and no prior attempt at resection (P = 0.01) as associated with 5-year survival. CONCLUSIONS: Our 27% actual 5-year survival rate for patients with resected PC is superior to that previously reported, and it is influenced by our emphasis on detailed staging and patient selection, a standardized operative approach, and routine use of multimodality therapy.

SURVIVAL PREDICTION FOR BRAIN TUMOR PATIENTS USING GENE EXPRESSION DATA

Brain tumor is one of the most aggressive types of cancer in humans, with an estimated median survival time of 12 months and only 4% of the patients surviving more than 5 years after disease diagnosis. Until recently, brain tumor prognosis has been based only on clinical information such as tumor grade and patient age, but there are reports indicating that molecular profiling of gliomas can reveal subgroups of patients with distinct survival rates. We hypothesize that coupling molecular profiling of brain tumors with clinical information might improve predictions of patient survival time and, consequently, better guide future treatment decisions. In order to evaluate this hypothesis, the general goal of this research is to build models for survival prediction of glioma patients using DNA molecular profiles (U133 Affymetrix gene expression microarrays) along with clinical information. First, a predictive Random Forest model is built for binary outcomes (i.e. short vs. long-term survival) and a small subset of genes whose expression values can be used to predict survival time is selected. Following, a new statistical methodology is developed for predicting time-to-death outcomes using Bayesian ensemble trees. Due to a large heterogeneity observed within prognostic classes obtained by the Random Forest model, prediction can be improved by relating time-to-death with gene expression profile directly. We propose a Bayesian ensemble model for survival prediction which is appropriate for high-dimensional data such as gene expression data. Our approach is based on the ensemble "sum-of-trees" model which is flexible to incorporate additive and interaction effects between genes. We specify a fully Bayesian hierarchical approach and illustrate our methodology for the CPH, Weibull, and AFT survival models. We overcome the lack of conjugacy using a latent variable formulation to model the covariate effects which decreases computation time for model fitting. Also, our proposed models provides a model-free way to select important predictive prognostic markers based on controlling false discovery rates. We compare the performance of our methods with baseline reference survival methods and apply our methodology to an unpublished data set of brain tumor survival times and gene expression data, selecting genes potentially related to the development of the disease under study. A closing discussion compares results obtained by Random Forest and Bayesian ensemble methods under the biological/clinical perspectives and highlights the statistical advantages and disadvantages of the new methodology in the context of DNA microarray data analysis.

Bifurcation and singularity analysis of a molecular network for the induction of long-term memory.

Withdrawal reflexes of the mollusk Aplysia exhibit sensitization, a simple form of long-term memory (LTM). Sensitization is due, in part, to long-term facilitation (LTF) of sensorimotor neuron synapses. LTF is induced by the modulatory actions of serotonin (5-HT). Pettigrew et al. developed a computational model of the nonlinear intracellular signaling and gene network that underlies the induction of 5-HT-induced LTF. The model simulated empirical observations that repeated applications of 5-HT induce persistent activation of protein kinase A (PKA) and that this persistent activation requires a suprathreshold exposure of 5-HT. This study extends the analysis of the Pettigrew model by applying bifurcation analysis, singularity theory, and numerical simulation. Using singularity theory, classification diagrams of parameter space were constructed, identifying regions with qualitatively different steady-state behaviors. The graphical representation of these regions illustrates the robustness of these regions to changes in model parameters. Because persistent protein kinase A (PKA) activity correlates with Aplysia LTM, the analysis focuses on a positive feedback loop in the model that tends to maintain PKA activity. In this loop, PKA phosphorylates a transcription factor (TF-1), thereby increasing the expression of an ubiquitin hydrolase (Ap-Uch). Ap-Uch then acts to increase PKA activity, closing the loop. This positive feedback loop manifests multiple, coexisting steady states, or multiplicity, which provides a mechanism for a bistable switch in PKA activity. After the removal of 5-HT, the PKA activity either returns to its basal level (reversible switch) or remains at a high level (irreversible switch). Such an irreversible switch might be a mechanism that contributes to the persistence of LTM. The classification diagrams also identify parameters and processes that might be manipulated, perhaps pharmacologically, to enhance the induction of memory. Rational drug design, to affect complex processes such as memory formation, can benefit from this type of analysis.

A model of the roles of essential kinases in the induction and expression of late long-term potentiation.

The induction of late long-term potentiation (L-LTP) involves complex interactions among second-messenger cascades. To gain insights into these interactions, a mathematical model was developed for L-LTP induction in the CA1 region of the hippocampus. The differential equation-based model represents actions of protein kinase A (PKA), MAP kinase (MAPK), and CaM kinase II (CAMKII) in the vicinity of the synapse, and activation of transcription by CaM kinase IV (CAMKIV) and MAPK. L-LTP is represented by increases in a synaptic weight. Simulations suggest that steep, supralinear stimulus-response relationships between stimuli (e.g., elevations in [Ca(2+)]) and kinase activation are essential for translating brief stimuli into long-lasting gene activation and synaptic weight increases. Convergence of multiple kinase activities to induce L-LTP helps to generate a threshold whereby the amount of L-LTP varies steeply with the number of brief (tetanic) electrical stimuli. The model simulates tetanic, -burst, pairing-induced, and chemical L-LTP, as well as L-LTP due to synaptic tagging. The model also simulates inhibition of L-LTP by inhibition of MAPK, CAMKII, PKA, or CAMKIV. The model predicts results of experiments to delineate mechanisms underlying L-LTP induction and expression. For example, the cAMP antagonist RpcAMPs, which inhibits L-LTP induction, is predicted to inhibit ERK activation. The model also appears useful to clarify similarities and differences between hippocampal L-LTP and long-term synaptic strengthening in other systems.

A model of late long-term potentiation simulates aspects of memory maintenance.

Late long-term potentiation (L-LTP) denotes long-lasting strengthening of synapses between neurons. L-LTP appears essential for the formation of long-term memory, with memories at least partly encoded by patterns of strengthened synapses. How memories are preserved for months or years, despite molecular turnover, is not well understood. Ongoing recurrent neuronal activity, during memory recall or during sleep, has been hypothesized to preferentially potentiate strong synapses, preserving memories. This hypothesis has not been evaluated in the context of a mathematical model representing ongoing activity and biochemical pathways important for L-LTP. In this study, ongoing activity was incorporated into two such models - a reduced model that represents some of the essential biochemical processes, and a more detailed published model. The reduced model represents synaptic tagging and gene induction simply and intuitively, and the detailed model adds activation of essential kinases by Ca(2+). Ongoing activity was modeled as continual brief elevations of Ca(2+). In each model, two stable states of synaptic strength/weight resulted. Positive feedback between synaptic weight and the amplitude of ongoing Ca(2+) transients underlies this bistability. A tetanic or theta-burst stimulus switches a model synapse from a low basal weight to a high weight that is stabilized by ongoing activity. Bistability was robust to parameter variations in both models. Simulations illustrated that prolonged periods of decreased activity reset synaptic strengths to low values, suggesting a plausible forgetting mechanism. However, episodic activity with shorter inactive intervals maintained strong synapses. Both models support experimental predictions. Tests of these predictions are expected to further understanding of how neuronal activity is coupled to maintenance of synaptic strength. Further investigations that examine the dynamics of activity and synaptic maintenance can be expected to help in understanding how memories are preserved for up to a lifetime in animals including humans.

Glutamate iontophoresis induces long-term potentiation in the absence of evoked presynaptic activity

$\rm\underline{L}$ong-$\rm\underline{t}$erm $\rm\underline{p}$otentiation (LTP) is a candidate cellular mechanism underlying mammalian learning and memory. Protocols that induce LTP typically involve afferent stimulation. The experiments described in this dissertation tested the hypothesis that LTP induction does not require presynaptic activity. The significance of this hypothesis is underscored by results suggesting that LTP expression may involve activity-dependent presynaptic changes. An induction protocol using glutamate iontophoresis was developed that reliably induces LTP in hippocampal slices without afferent stimulation (ionto-LTP). Ionto-LTP is induced when excitatory postsynaptic potentials are completely blocked with adenosine and $\rm\underline{t}$etrodo$\rm\underline{t}$o$\rm\underline{x}$in (TTX). These results suggest constraints on the involvement of presynaptic mechanisms and putative retrograde messengers in LTP induction and expression; namely, these processes must function without many forms of activity-dependent presynaptic processes.^ In testing the role of pre-and postsynaptic mechanisms in LTP expression whole-cell recordings were used to examine the frequency and amplitude of $\rm\underline{s}$pontaneous $\rm\underline{e}$xcitatory $\rm\underline{p}$o$\rm\underline{s}$ynaptic $\rm\underline{c}$urrents (sEPSCs) in CA1 pyramidal neurons. sEPSCs where comprised of an equal mixture of TTX insensitive miniature EPSCs and sEPSCs that appeared to result from spontaneous action potentials (i.e., TTX sensitive EPSCs). The detection of all sEPSCs was virtually eliminated by CNQX, suggesting that sEPSCs were glutamate mediated synaptic events. Changes in the amplitude and frequency sEPSCs were examined during the expression of ionto-LTP to obtain new information about the cellular location of mechanisms involved in synaptic plasticity. The findings of this dissertation show that ionto-LTP expression results from increased sEPSC amplitude in the absence of lasting increases in sEPSC frequency. Potentiation of sEPSC amplitude without changes in sEPSC frequency has been previously interpreted to be due to postsynaptic mechanisms. Although this interpretation is supported by findings from peripheral synapses, its application to the central nervous system is unclear. Therefore, alternative mechanisms are also considered in this dissertation. Models based on increased release probability for action potential dependent transmitter release appear insufficient to explain our results. The most straightforward interpretation of the results in this dissertation is that LTP induced by glutamate iontophoresis on dendrites of CA1 pyramidal neurons is mediated by postsynaptic mechanisms. ^

Signals underlying the induction and expression of long-term, injury-related plasticity in sensory neurons of Aplysia

An important goal in the study of long-term memory is to understand the signals that induce and maintain the underlying neural alterations. In Aplysia, long-term sensitization of defensive reflexes has been examined in depth as a simple model of memory. Extensive studies of sensory neurons (SNs) in Aplysia have led to a cellular and molecular model of long-term memory that has greatly influenced memory research. According to this model, induction of long-term memory in Aplysia depends upon serotonin (5-HT) release and subsequent activation of the cAMP-PKA pathway in SNs. The evidence supporting this model mainly came from studies of long-term synaptic facilitation (LTF) using dissociated (and therefore axotomized) cells growing in culture. However, studies in more intact preparations have produced complex and discrepant results. Because these SNs function as nociceptors, and display similar alterations (long-term hyperexcitability [LTH], LTF, and growth) in models of memory and nerve injury, this study examined the roles of 5-HT and the cAMP-PKA pathway in the induction and expression of long-term, injury-related LTH and LTF in Aplysia SNs. ^ The results presented here suggest that 5-HT is not a primary signal for inducing LTH (and perhaps LTF) in Aplysia SNs. Prolonged treatment with 5-HT failed to induce LTH of Aplysia SNs in either ganglia or dissociated-cell preparations. Treatment with a 5-HT antagonist, methiothepin, during noxious nerve stimulation failed to reduce 24 hr LTH. Furthermore, while 5-HT can induce LTF of SN synapses, this LTF appears to be an indirect effect of 5-HT on other cells. When neural activity was suppressed by elevating divalent cations or by using tetrodotoxin (TTX), 5-HT failed to induce LTF. Unlike LTF, LTH of the SNs could not be produced, even when 5-HT treatment occurred in normal artificial sea water (ASW), suggesting that LTH and LTF are likely to depend on different signals for induction. However, methiothepin reduced the later expression of LTH induced by nerve stimulation, suggesting that 5-HT contributes to the maintenance of LTH in Aplysia SNs.n of somata from the ganglion (which axotomizes SNs) or crushing peripheral n. ^ In summary, this study found that 5-HT and the cAMP-PKA pathway are not involved in the induction of long-term, injury-related LTH of Aplysia SNs, but persistent release of 5-HT and persistent PKA activity contribute to the maintenance of LTH induced by injury. (Abstract shortened by UMI.)^

COSTS AND EFFECTS OF ARKANSAS' LONG-TERM CARE DEMONSTRATION PROJECT (MEDICAID, CHANELLING, COMMUNITY ASSESSMENT)

The purpose of this study was to assess the impact of the Arkansas Long-Term Care Demonstration Project upon Arkansas' Medicaid expenditures and upon the clients it serves. A Retrospective Medicaid expenditure study component used analyses of variance techniques to test for the Project's effects upon aggregated expenditures for 28 demonstration and control counties representing 25 percent of the State's population over four years, 1979-1982.^ A second approach to the study question utilized a 1982 prospective sample of 458 demonstration and control clients from the same 28 counties. The disability level or need for care of each patient was established a priori. The extent to which an individual's variation in Medicaid utilization and costs was explained by patient need, presence or absence of the channeling project's placement decision or some other patient characteristic was examined by multiple regression analysis. Long-term and acute care Medicaid, Medicare, third party, self-pay and the grand total of all Medicaid claims were analyzed for project effects and explanatory relationships.^ The main project effect was to increase personal care costs without reducing nursing home or acute care costs (Prospective Study). Expansion of clients appeared to occur in personal care (Prospective Study) and minimum care nursing home (Retrospective Study) for the project areas. Cost-shifting between Medicaid and Medicare in the project areas and two different patterns of utilization in the North and South projects tended to offset each other such that no differences in total costs between the project areas and demonstration areas occurred. The project was significant ((beta) = .22, p < .001) only for personal care costs. The explanatory power of this personal care regression model (R('2) = .36) was comparable to other reported health services utilization models. Other variables (Medicare buy-in, level of disability, Social Security Supplemental Income (SSI), net monthly income, North/South areas and age) explained more variation in the other twelve cost regression models. ^

Mixture modeling for joint analysis of survival, discrete, and continuous data

Mixture modeling is commonly used to model categorical latent variables that represent subpopulations in which population membership is unknown but can be inferred from the data. In relatively recent years, the potential of finite mixture models has been applied in time-to-event data. However, the commonly used survival mixture model assumes that the effects of the covariates involved in failure times differ across latent classes, but the covariate distribution is homogeneous. The aim of this dissertation is to develop a method to examine time-to-event data in the presence of unobserved heterogeneity under a framework of mixture modeling. A joint model is developed to incorporate the latent survival trajectory along with the observed information for the joint analysis of a time-to-event variable, its discrete and continuous covariates, and a latent class variable. It is assumed that the effects of covariates on survival times and the distribution of covariates vary across different latent classes. The unobservable survival trajectories are identified through estimating the probability that a subject belongs to a particular class based on observed information. We applied this method to a Hodgkin lymphoma study with long-term follow-up and observed four distinct latent classes in terms of long-term survival and distributions of prognostic factors. Our results from simulation studies and from the Hodgkin lymphoma study demonstrated the superiority of our joint model compared with the conventional survival model. This flexible inference method provides more accurate estimation and accommodates unobservable heterogeneity among individuals while taking involved interactions between covariates into consideration.^