Procedure for the identification of population characteristics for targeting intervention services on the basis of outcome-goal priorities: Case management services for the chronic mentally ill

Although the processes involved in rational patient targeting may be obvious for certain services, for others, both the appropriate sub-populations to receive services and the procedures to be used for their identification may be unclear. This project was designed to address several research questions which arise in the attempt to deliver appropriate services to specific populations. The related difficulties are particularly evident for those interventions about which findings regarding effectiveness are conflicting. When an intervention clearly is not beneficial (or is dangerous) to a large, diverse population, consensus regarding withholding the intervention from dissemination can easily be reached. When findings are ambiguous, however, conclusions may be impossible.^ When characteristics of patients likely to benefit from an intervention are not obvious, and when the intervention is not significantly invasive or dangerous, the strategy proposed herein may be used to identify specific characteristics of sub-populations which may benefit from the intervention. The identification of these populations may be used both in further informing decisions regarding distribution of the intervention and for purposes of planning implementation of the intervention by identifying specific target populations for service delivery.^ This project explores a method for identifying such sub-populations through the use of related datasets generated from clinical trials conducted to test the effectiveness of an intervention. The method is specified in detail and tested using the example intervention of case management for outpatient treatment of populations with chronic mental illness. These analyses were applied in order to identify any characteristics which distinguish specific sub-populations who are more likely to benefit from case management service, despite conflicting findings regarding its effectiveness for the aggregate population, as reported in the body of related research. However, in addition to a limited set of characteristics associated with benefit, the findings generated, a larger set of characteristics of patients likely to experience greater improvement without intervention. ^

An Innovative Phase I Trial Design Allowing for the Identification of Multiple Potential Maximum Tolerated Doses with Combination Therapy of Targeted Agents

Treatment for cancer often involves combination therapies used both in medical practice and clinical trials. Korn and Simon listed three reasons for the utility of combinations: 1) biochemical synergism, 2) differential susceptibility of tumor cells to different agents, and 3) higher achievable dose intensity by exploiting non-overlapping toxicities to the host. Even if the toxicity profile of each agent of a given combination is known, the toxicity profile of the agents used in combination must be established. Thus, caution is required when designing and evaluating trials with combination therapies. Traditional clinical design is based on the consideration of a single drug. However, a trial of drugs in combination requires a dose-selection procedure that is vastly different than that needed for a single-drug trial. When two drugs are combined in a phase I trial, an important trial objective is to determine the maximum tolerated dose (MTD). The MTD is defined as the dose level below the dose at which two of six patients experience drug-related dose-limiting toxicity (DLT). In phase I trials that combine two agents, more than one MTD generally exists, although all are rarely determined. For example, there may be an MTD that includes high doses of drug A with lower doses of drug B, another one for high doses of drug B with lower doses of drug A, and yet another for intermediate doses of both drugs administered together. With classic phase I trial designs, only one MTD is identified. Our new trial design allows identification of more than one MTD efficiently, within the context of a single protocol. The two drugs combined in our phase I trial are temsirolimus and bevacizumab. Bevacizumab is a monoclonal antibody targeting the vascular endothelial growth factor (VEGF) pathway which is fundamental for tumor growth and metastasis. One mechanism of tumor resistance to antiangiogenic therapy is upregulation of hypoxia inducible factor 1α (HIF-1α) which mediates responses to hypoxic conditions. Temsirolimus has resulted in reduced levels of HIF-1α making this an ideal combination therapy. Dr. Donald Berry developed a trial design schema for evaluating low, intermediate and high dose levels of two drugs given in combination as illustrated in a recently published paper in Biometrics entitled “A Parallel Phase I/II Clinical Trial Design for Combination Therapies.” His trial design utilized cytotoxic chemotherapy. We adapted this design schema by incorporating greater numbers of dose levels for each drug. Additional dose levels are being examined because it has been the experience of phase I trials that targeted agents, when given in combination, are often effective at dosing levels lower than the FDA-approved dose of said drugs. A total of thirteen dose levels including representative high, intermediate and low dose levels of temsirolimus with representative high, intermediate, and low dose levels of bevacizumab will be evaluated. We hypothesize that our new trial design will facilitate identification of more than one MTD, if they exist, efficiently and within the context of a single protocol. Doses gleaned from this approach could potentially allow for a more personalized approach in dose selection from among the MTDs obtained that can be based upon a patient’s specific co-morbid conditions or anticipated toxicities.

The story of RP10: A positional cloning approach to the identification of an autosomal dominant retinitis pigmentosa gene

Retinitis pigmentosa (RP) is an inherited retinal degenerative disease that is the leading cause of inherited blindness worldwide. Characteristic features of the disease include night blindness, progressive loss of visual fields, and deposition of pigment on the retina in a bone spicule-like pattern. RP is marked by extreme genetic heterogeneity with at least 19 autosomal dominant, autosomal recessive and X-linked loci identified. RP10, which maps to chromosome 7q, was the fifth autosomal dominant RP locus identified, and accounts for the early-onset disease in two independent families. Extensive linkage and haplotype analyses have been performed in these two families which have allowed the assignment of the disease locus to a 5-cM region on chromosome 7q31.3. In collaboration with Dr. Eric Green (National Center for Human Genome Research, National Institutes of Health), a well-characterized physical map of the region was constructed which includes YAC, BAC and cosmid coverage. The entire RP10 critical region resides within a 9-Mb well-characterized YAC contig. These physical maps not only provided the resources to undertake the CAIGES (cDNA amplification for identification of genomic expressed sequences) procedure for identification of retinal candidate genes within the critical region, but also identified a number of candidate genes, including transducin-$\gamma$ and blue cone pigment genes. All candidate genes examined were excluded. In addition, a number of ESTs were mapped within the critical region. EST20241, which was isolated from an eye library, corresponded to the 3$\sp\prime$ region of the ADP-ribosylation factor (ARF) 5 gene. ARF5, with its role in vesicle transport and possible participation in the regulation of the visual transduction pathway, became an extremely interesting candidate gene. Using a primer walking approach, the entire 3.2 kb genomic sequence of the ARF5 gene was generated and developed intronic primers to screen for coding region mutations in affected family members. No mutations were found in the ARF5 gene, however, a number of additional ESTs have been mapped to the critical region, and, as the large-scale sequencing projects get underway, megabases of raw sequence data from the RP10 region are becoming available. These resources will hasten the isolation and characterization of the RP10 gene. ^

INTAKE VARIABLES AS PREDICTORS OF ASSESSMENT AND REFERRAL PROCEDURE OUTCOMES IN EMPLOYEE ASSISTANCE PROGRAMS

While reported prevalence rates of troubled employees vary considerably, even conservative estimates indicate a major public health problem. For example, alcohol and drug related problems alone cost U.S. industry more than 45 billion dollars annually.^ Of the alternatives available to deal with these problems, e.g., dismissal or disciplinary actions, the most viable and cost effective are employee assistance programs (EAP), designed to provide professional assistance to employees experiencing alcohol, drug, emotional or personal crisis.^ The principal component of an EAP is that of assessment and referral, and this study was developed to determine which EAP client intake variables are the most efficacious predictors of assessment and referral procedures.^ Although, specific client intake variables were statistically significant the discriminant classification analysis was demonstrably inadequate. Nevertheless, the identification of A/R procedure phases which were not efficacious, as well as EAP client populations for whom services were not effective, were extremely valuable discernments. Identifying the less efficacious components of the A/R process provided an opportunity to recommend alternatives to current program procedures and practices, which may ameliorate program effectiveness. ^

INTEGRATIVE BIOMARKER IDENTIFICATION AND CLASSIFICATION USING HIGH THROUGHPUT ASSAYS

It is well accepted that tumorigenesis is a multi-step procedure involving aberrant functioning of genes regulating cell proliferation, differentiation, apoptosis, genome stability, angiogenesis and motility. To obtain a full understanding of tumorigenesis, it is necessary to collect information on all aspects of cell activity. Recent advances in high throughput technologies allow biologists to generate massive amounts of data, more than might have been imagined decades ago. These advances have made it possible to launch comprehensive projects such as (TCGA) and (ICGC) which systematically characterize the molecular fingerprints of cancer cells using gene expression, methylation, copy number, microRNA and SNP microarrays as well as next generation sequencing assays interrogating somatic mutation, insertion, deletion, translocation and structural rearrangements. Given the massive amount of data, a major challenge is to integrate information from multiple sources and formulate testable hypotheses. This thesis focuses on developing methodologies for integrative analyses of genomic assays profiled on the same set of samples. We have developed several novel methods for integrative biomarker identification and cancer classification. We introduce a regression-based approach to identify biomarkers predictive to therapy response or survival by integrating multiple assays including gene expression, methylation and copy number data through penalized regression. To identify key cancer-specific genes accounting for multiple mechanisms of regulation, we have developed the integIRTy software that provides robust and reliable inferences about gene alteration by automatically adjusting for sample heterogeneity as well as technical artifacts using Item Response Theory. To cope with the increasing need for accurate cancer diagnosis and individualized therapy, we have developed a robust and powerful algorithm called SIBER to systematically identify bimodally expressed genes using next generation RNAseq data. We have shown that prediction models built from these bimodal genes have the same accuracy as models built from all genes. Further, prediction models with dichotomized gene expression measurements based on their bimodal shapes still perform well. The effectiveness of outcome prediction using discretized signals paves the road for more accurate and interpretable cancer classification by integrating signals from multiple sources.