TP53 as a Biomarker in Head and Neck Squamous Cell Carcinoma

Currently, there are no molecular biomarkers that guide treatment decisions for patients with head and neck squamous cell carcinoma (HNSCC). Several retrospective studies have evaluated TP53 in HNSCC, and results have suggested that specific mutations are associated with poor outcome. However, there exists heterogeneity among these studies in the site and stage of disease of the patients reviewed, the treatments rendered, and methods of evaluating TP53 mutation. Thus, it remains unclear as to which patients and in which clinical settings TP53 mutation is most useful in predicting treatment failure. In the current study, we reviewed the records of a cohort of patients with advanced, resectable HNSCC who received surgery and post-operative radiation (PORT) and had DNA isolated from fresh tumor tissue obtained at the time of surgery. TP53 mutations were identified using Sanger sequencing of exons 2-11 and the associated splice regions of the TP53 gene. We have found that the group of patients with either non-disruptive or disruptive TP53 mutations had decreased overall survival, disease-free survival, and an increased rate of distant metastasis. When examined as an independent factor, disruptive mutation was strongly associated with the development of distant metastasis. As a second aim of this project, we performed a pilot study examining the utility of the AmpliChip® p53 test as a practical method for TP53 sequencing in the clinical setting. AmpliChip® testing and Sanger sequencing was performed on a separate cohort of patients with HNSCC. Our study demonstrated the ablity of the AmpliChip® to call TP53 mutation from a single formalin-fixed paraffin-embedded slide. The results from AmpliChip® testing were identical with the Sanger method in 11 of 19 cases, with a higher rate of mutation calls using the AmpliChip® test. TP53 mutation is a potential prognostic biomarker among patients with advanced, resectable HNSCC treated with surgery and PORT. Whether this subgroup of patients could benefit from the addition of concurrent or induction chemotherapy remains to be evaluated in prospective clinical trials. Our pilot study of the p53 AmpliChip® suggests this could be a practical and reliable method of TP53 analysis in the clinical setting.

Dimethylsphingosine evokes diverse cellular death modes in HL60

Among the barriers to successful cancer treatment is the acquired resistance that tumors undergo due to clonal evolution. Non-cross resistant drugs could add to the current options of chemotherapeutic drugs. In order to improve tumor response, investigators have been identifying defective death pathways acquired by specific cancer types, so to target directly those pathways. Sphingolipids have emerged as potential drugs for tumor-targeted therapy, and among them, Dimethylsphingosine (DMS), known to be a competitive inhibitor of Sphingosine Kinase (SK). DMS actions have been documented by several investigators, but the mechanisms by which DMS exerts cytotoxicity have not been fully investigated. We evaluated the cytotoxicity of DMS against human leukemia cell lines and against blasts isolated from leukemia patients. Cell line viability decreased proportionally to DMS concentration and treatment time. Resistant and MDR positive cell lines were the most sensitive, indicating DMS efficacy against human leukemia MDR. Importantly, leukemia samples showed a similar sensitivity to DMS, the first demonstration of DMS activity against fresh human leukemia specimens. Mechanistically we have demonstrated that DMS efficacy is due to its ability to induce cytotoxicity by inducing necrosis, apoptosis or both concomitantly, revealing a mixed-feature cell death mode never described before for DMS. Further, we have shown evidence suggesting pathways cross-talk, since apoptosis inhibition led to accelerated rate of necrosis. DMS diverse killing mechanisms and the high expression of SK in leukemias could explain DMS potent cytotoxicity. DMS-based regimens may increase response rates and therefore, improve leukemia treatment. ^

The Open Door Mission: Measuring and predicting outcomes of one community-based substance abuse treatment program

The objectives of this study were to identify and measure the average outcomes of the Open Door Mission's nine-month community-based substance abuse treatment program, identify predictors of successful outcomes, and make recommendations to the Open Door Mission for improving its treatment program.^ The Mission's program is exclusive to adult men who have limited financial resources: most of which were homeless or dependent on parents or other family members for basic living needs. Many, but not all, of these men are either chemically dependent or have a history of substance abuse.^ This study tracked a cohort of the Mission's graduates throughout this one-year study and identified various indicators of success at short-term intervals, which may be predictive of longer-term outcomes. We tracked various levels of 12-step program involvement, as well as other social and spiritual activities, such as church affiliation and recovery support.^ Twenty-four of the 66 subjects, or 36% met the Mission's requirements for success. Specific to this success criteria; Fifty-four, or 82% reported affiliation with a home church; Twenty-six, or 39% reported full-time employment; Sixty-one, or 92% did not report or were not identified as having any post-treatment arrests or incarceration, and; Forty, or 61% reported continuous abstinence from both drugs and alcohol.^ Five research-based hypotheses were developed and tested. The primary analysis tool was the web-based non-parametric dependency modeling tool, B-Course, which revealed some strong associations with certain variables, and helped the researchers generate and test several data-driven hypotheses. Full-time employment is the greatest predictor of abstinence: 95% of those who reported full time employment also reported continuous post-treatment abstinence, while 50% of those working part-time were abstinent and 29% of those with no employment were abstinent. Working with a 12-step sponsor, attending aftercare, and service with others were identified as predictors of abstinence.^ This study demonstrates that associations with abstinence and the ODM success criteria are not simply based on one social or behavioral factor. Rather, these relationships are interdependent, and show that abstinence is achieved and maintained through a combination of several 12-step recovery activities. This study used a simple assessment methodology, which demonstrated strong associations across variables and outcomes, which have practical applicability to the Open Door Mission for improving its treatment program. By leveraging the predictive capability of the various success determination methodologies discussed and developed throughout this study, we can identify accurate outcomes with both validity and reliability. This assessment instrument can also be used as an intervention that, if operationalized to the Mission’s clients during the primary treatment program, may measurably improve the effectiveness and outcomes of the Open Door Mission.^

TIME DEPENDENT ALTERATIONS IN CENTRAL NERVOUS SYSTEM DOPAMINE RECEPTORS INDUCED BY DOPAMINE AGONIST TREATMENT

Levodopa, the precursor of dopamine, is currently the drug of choice in the treatment of Parkinson's disease. Recently, two direct dopamine agonists, bromocriptine and pergolide, have been tested for the treatment of Parkinson's disease because of reduced side effects compared to levodopa. Few studies have evaluated the effects of long-term treatment of dopamine agonists on dopamine receptor regulation in the central nervous system. Thus, the purpose of this study was to determine whether chronic dopamine agonist treatment produces a down-regulation of striatal dopamine receptor function and to compare the results of the two classes of dopaminergic drugs.^ Levodopa with carbidopa, a peripheral decarboxylase inhibitor, was administered orally to rats whereas bromocriptine and pergolide were injected intraperitoneally once daily. Several neurochemical parameters were examined from 1 to 28 days.^ Levodopa minimally decreased striatal D-1 receptor activity but increased the number of striatal D-2 binding sites. Levodopa increased the V(,max) of tyrosine hydroxylase (TH) in all brain regions tested. Protein blot analysis of striatal TH indicated a significant increase in the amount of TH present. Dopamine-beta-hydroxylase (DBH) activity was markedly decreased in all brain regions studied and mixing experiments of control and drug-treated cortices did not show the presence of an increased level of endogenous inhibitors.^ Bromocriptine treatment decreased the number of D-2 binding sites. Striatal TH activity was decreased and protein blot analysis indicated no change in TH quantity. The specificity of bromocriptine for striatal TH suggested that bromocriptine preferentially interacts with dopamine autoreceptors.^ Combination levodopa-bromocriptine was administered for 12 days. There was a decrease in both D-1 receptor activity and D-2 binding sites, and a decrease in brain HVA levels suggesting a postsynaptic receptor action. Pergolide produced identical results to the combination levodopa-bromocriptine studies.^ In conclusion, combination levodopa-bromocriptine and pergolide treatments exhibited the expected down-regulation of dopamine receptor activity. In contrast, levodopa appeared to up-regulate dopamine receptor activity. Thus, these data may help to explain, on a biochemical basis, the decrease in the levodopa-induced side effects noted with combination levodopa-bromocriptine or pergolide therapies in the treatment of Parkinson's disease. ^

The role of workplace absence as an organizational mechanism in predicting employee alcohol and drug disorders

Background. EAP programs for airline pilots in companies with a well developed recovery management program are known to reduce pilot absenteeism following treatment. Given the costs and safety consequences to society, it is important to identify pilots who may be experiencing an AOD disorder to get them into treatment. ^ Hypotheses. This study investigated the predictive power of workplace absenteeism in identifying alcohol or drug disorders (AOD). The first hypothesis was that higher absenteeism in a 12-month period is associated with higher risk that an employee is experiencing AOD. The second hypothesis was that AOD treatment would reduce subsequent absence rates and the costs of replacing pilots on missed flights. ^ Methods. A case control design using eight years (time period) of monthly archival absence data (53,000 pay records) was conducted with a sample of (N = 76) employees having an AOD diagnosis (cases) matched 1:4 with (N = 304) non-diagnosed employees (controls) of the same profession and company (male commercial airline pilots). Cases and controls were matched on the variables age, rank and date of hire. Absence rate was defined as sick time hours used over the sum of the minimum guarantee pay hours annualized using the months the pilot worked for the year. Conditional logistic regression was used to determine if absence predicts employees experiencing an AOD disorder, starting 3 years prior to the cases receiving the AOD diagnosis. A repeated measures ANOVA, t tests and rate ratios (with 95% confidence intervals) were conducted to determine differences between cases and controls in absence usage for 3 years pre and 5 years post treatment. Mean replacement costs were calculated for sick leave usage 3 years pre and 5 years post treatment to estimate the cost of sick leave from the perspective of the company. ^ Results. Sick leave, as measured by absence rate, predicted the risk of being diagnosed with an AOD disorder (OR 1.10, 95% CI = 1.06, 1.15) during the 12 months prior to receiving the diagnosis. Mean absence rates for diagnosed employees increased over the three years before treatment, particularly in the year before treatment, whereas the controls’ did not (three years, x = 6.80 vs. 5.52; two years, x = 7.81 vs. 6.30, and one year, x = 11.00cases vs. 5.51controls. In the first year post treatment compared to the year prior to treatment, rate ratios indicated a significant (60%) post treatment reduction in absence rates (OR = 0.40, CI = 0.28, 0.57). Absence rates for cases remained lower than controls for the first three years after completion of treatment. Upon discharge from the FAA and company’s three year AOD monitoring program, case’s absence rates increased slightly during the fourth year (controls, x = 0.09, SD = 0.14, cases, x = 0.12, SD = 0.21). However, the following year, their mean absence rates were again below those of the controls (controls, x = 0.08, SD = 0.12, cases, x¯ = 0.06, SD = 0.07). Significant reductions in costs associated with replacing pilots calling in sick, were found to be 60% less, between the year of diagnosis for the cases and the first year after returning to work. A reduction in replacement costs continued over the next two years for the treated employees. ^ Conclusions. This research demonstrates the potential for workplace absences as an active organizational surveillance mechanism to assist managers and supervisors in identifying employees who may be experiencing or at risk of experiencing an alcohol/drug disorder. Currently, many workplaces use only performance problems and ignore the employee’s absence record. A referral to an EAP or alcohol/drug evaluation based on the employee’s absence/sick leave record as incorporated into company policy can provide another useful indicator that may also carry less stigma, thus reducing barriers to seeking help. This research also confirms two conclusions heretofore based only on cross-sectional studies: (1) higher absence rates are associated with employees experiencing an AOD disorder; (2) treatment is associated with lower costs for replacing absent pilots. Due to the uniqueness of the employee population studied (commercial airline pilots) and the organizational documentation of absence, the generalizability of this study to other professions and occupations should be considered limited. ^ Transition to Practice. The odds ratios for the relationship between absence rates and an AOD diagnosis are precise; the OR for year of diagnosis indicates the likelihood of being diagnosed increases 10% for every hour change in sick leave taken. In practice, however, a pilot uses approximately 20 hours of sick leave for one trip, because the replacement will have to be paid the guaranteed minimum of 20 hour. Thus, the rate based on hourly changes is precise but not practical. ^ To provide the organization with practical recommendations the yearly mean absence rates were used. A pilot flies on average, 90 hours a month, 1080 annually. Cases used almost twice the mean rate of sick time the year prior to diagnosis (T-1) compared to controls (cases, x = .11, controls, x = .06). Cases are expected to use on average 119 hours annually (total annual hours*mean annual absence rate), while controls will use 60 hours. The cases’ 60 hours could translate to 3 trips of 20 hours each. Management could use a standard of 80 hours or more of sick time claimed in a year as the threshold for unacceptable absence, a 25% increase over the controls (a cost to the company of approximately of $4000). At the 80-hour mark, the Chief Pilot would be able to call the pilot in for a routine check as to the nature of the pilot’s excessive absence. This management action would be based on a company standard, rather than a behavioral or performance issue. Using absence data in this fashion would make it an active surveillance mechanism. ^

Impact of real time polymerase chain reaction technology on the therapeutic approach to treatment of staphylococcal infections

Can the early identification of the species of staphylococcus responsible for infection by the use of Real Time PCR technology influence the approach to the treatment of these infections? ^ This study was a retrospective cohort study in which two groups of patients were compared. The first group, ‘Physician Aware’ consisted of patients in whom physicians were informed of specific staphylococcal species and antibiotic sensitivity (using RT-PCR) at the time of notification of the gram stain. The second group, ‘Physician Unaware’ consisted of patients in whom treating physicians received the same information 24–72 hours later as a result of blood culture and antibiotic sensitivity determination. ^ The approach to treatment was compared between ‘Physician Aware’ and ‘Physician Unaware’ groups for three different microbiological diagnoses—namely MRSA, MSSA and no-SA (or coagulase negative Staphylococcus). ^ For a diagnosis of MRSA, the mean time interval to the initiation of Vancomycin therapy was 1.08 hours in the ‘Physician Aware’ group as compared to 5.84 hours in the ‘Physician Unaware’ group (p=0.34). ^ For a diagnosis of MSSA, the mean time interval to the initiation of specific anti-MSSA therapy with Nafcillin was 5.18 hours in the ‘Physician Aware’ group as compared to 49.8 hours in the ‘Physician Unaware’ group (p=0.007). Also, for the same diagnosis, the mean duration of empiric therapy in the ‘Physician Aware’ group was 19.68 hours as compared to 80.75 hours in the ‘Physician Unaware’ group (p=0.003) ^ For a diagnosis of no-SA or coagulase negative staphylococcus, the mean duration of empiric therapy was 35.65 hours in the ‘Physician Aware’ group as compared to 44.38 hours in the ‘Physician Unaware’ group (p=0.07). However, when treatment was considered a categorical variable and after exclusion of all cases where anti-MRS therapy was used for unrelated conditions, only 20 of 72 cases in the ‘Physician Aware’ group received treatment as compared to 48 of 106 cases in the ‘Physician Unaware’ group. ^ Conclusions. Earlier diagnosis of MRSA may not alter final treatment outcomes. However, earlier identification may lead to the earlier institution of measures to limit the spread of infection. The early diagnosis of MSSA infection, does lead to treatment with specific antibiotic therapy at an earlier stage of treatment. Also, the duration of empiric therapy is greatly reduced by early diagnosis. The early diagnosis of coagulase negative staphylococcal infection leads to a lower rate of unnecessary treatment for these infections as they are commonly considered contaminants. ^

TIME SERIES ANALYSIS AS INPUT FOR PREDICTIVE MODELING: PREDICTING CARDIAC ARREST IN A PEDIATRIC INTENSIVE CARE UNIT

The first manuscript, entitled "Time-Series Analysis as Input for Clinical Predictive Modeling: Modeling Cardiac Arrest in a Pediatric ICU" lays out the theoretical background for the project. There are several core concepts presented in this paper. First, traditional multivariate models (where each variable is represented by only one value) provide single point-in-time snapshots of patient status: they are incapable of characterizing deterioration. Since deterioration is consistently identified as a precursor to cardiac arrests, we maintain that the traditional multivariate paradigm is insufficient for predicting arrests. We identify time series analysis as a method capable of characterizing deterioration in an objective, mathematical fashion, and describe how to build a general foundation for predictive modeling using time series analysis results as latent variables. Building a solid foundation for any given modeling task involves addressing a number of issues during the design phase. These include selecting the proper candidate features on which to base the model, and selecting the most appropriate tool to measure them. We also identified several unique design issues that are introduced when time series data elements are added to the set of candidate features. One such issue is in defining the duration and resolution of time series elements required to sufficiently characterize the time series phenomena being considered as candidate features for the predictive model. Once the duration and resolution are established, there must also be explicit mathematical or statistical operations that produce the time series analysis result to be used as a latent candidate feature. In synthesizing the comprehensive framework for building a predictive model based on time series data elements, we identified at least four classes of data that can be used in the model design. The first two classes are shared with traditional multivariate models: multivariate data and clinical latent features. Multivariate data is represented by the standard one value per variable paradigm and is widely employed in a host of clinical models and tools. These are often represented by a number present in a given cell of a table. Clinical latent features derived, rather than directly measured, data elements that more accurately represent a particular clinical phenomenon than any of the directly measured data elements in isolation. The second two classes are unique to the time series data elements. The first of these is the raw data elements. These are represented by multiple values per variable, and constitute the measured observations that are typically available to end users when they review time series data. These are often represented as dots on a graph. The final class of data results from performing time series analysis. This class of data represents the fundamental concept on which our hypothesis is based. The specific statistical or mathematical operations are up to the modeler to determine, but we generally recommend that a variety of analyses be performed in order to maximize the likelihood that a representation of the time series data elements is produced that is able to distinguish between two or more classes of outcomes. The second manuscript, entitled "Building Clinical Prediction Models Using Time Series Data: Modeling Cardiac Arrest in a Pediatric ICU" provides a detailed description, start to finish, of the methods required to prepare the data, build, and validate a predictive model that uses the time series data elements determined in the first paper. One of the fundamental tenets of the second paper is that manual implementations of time series based models are unfeasible due to the relatively large number of data elements and the complexity of preprocessing that must occur before data can be presented to the model. Each of the seventeen steps is analyzed from the perspective of how it may be automated, when necessary. We identify the general objectives and available strategies of each of the steps, and we present our rationale for choosing a specific strategy for each step in the case of predicting cardiac arrest in a pediatric intensive care unit. Another issue brought to light by the second paper is that the individual steps required to use time series data for predictive modeling are more numerous and more complex than those used for modeling with traditional multivariate data. Even after complexities attributable to the design phase (addressed in our first paper) have been accounted for, the management and manipulation of the time series elements (the preprocessing steps in particular) are issues that are not present in a traditional multivariate modeling paradigm. In our methods, we present the issues that arise from the time series data elements: defining a reference time; imputing and reducing time series data in order to conform to a predefined structure that was specified during the design phase; and normalizing variable families rather than individual variable instances. The final manuscript, entitled: "Using Time-Series Analysis to Predict Cardiac Arrest in a Pediatric Intensive Care Unit" presents the results that were obtained by applying the theoretical construct and its associated methods (detailed in the first two papers) to the case of cardiac arrest prediction in a pediatric intensive care unit. Our results showed that utilizing the trend analysis from the time series data elements reduced the number of classification errors by 73%. The area under the Receiver Operating Characteristic curve increased from a baseline of 87% to 98% by including the trend analysis. In addition to the performance measures, we were also able to demonstrate that adding raw time series data elements without their associated trend analyses improved classification accuracy as compared to the baseline multivariate model, but diminished classification accuracy as compared to when just the trend analysis features were added (ie, without adding the raw time series data elements). We believe this phenomenon was largely attributable to overfitting, which is known to increase as the ratio of candidate features to class examples rises. Furthermore, although we employed several feature reduction strategies to counteract the overfitting problem, they failed to improve the performance beyond that which was achieved by exclusion of the raw time series elements. Finally, our data demonstrated that pulse oximetry and systolic blood pressure readings tend to start diminishing about 10-20 minutes before an arrest, whereas heart rates tend to diminish rapidly less than 5 minutes before an arrest.