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1.  Examining the Effect of Teleconferences on Oncology Phase 1 Trials 
Journal of Cancer  2013;4(6):464-467.
Objective: Phase 1 clinical trials are the first stage of clinical development of an investigational agent. Because the trials often take place at several geographically dispersed sites, safety teleconferences are held to update investigators and the drug sponsor on safety information and other pertinent business related to the trial conduct. Here we examine associations between the frequency of teleconferences and other clinical trial factors on trial conduct efficiency.
Methods: We examined Phase 1 clinical trials for patients with solid tumors opened for enrollment at a single, non-profit cancer center in Arizona (Center) that had completed at least three dose levels. The following information was included: safety teleconference frequency, whether or not the sponsor or contract research organization sent follow-up requests for updates on patient accrual, and safety outside of scheduled safety teleconferences. The dose escalation scheme, route of study drug administration and formulation type (e.g. oral targeted therapy or monoclonal antibody) was also included.
Results: Forty-nine Phase 1 studies were examined for inclusion. The majority of safety teleconferences were regularly scheduled (81.6%) with most taking place bi-weekly (46.9%). Additional solicitation for updates outside of scheduled safety teleconferences were requested during the conduct of 31 (63.3%) studies. None of the factors analyzed were significantly associated with accrual, subject dosing, and dose escalation.
Conclusion: We found that the frequency of teleconferences does not appear to expedite phase 1 study accrual, subject dosing, or dose escalation in the first 3 cohorts of a phase 1 clinical trial.
PMCID: PMC3726707  PMID: 23901345
Phase 1 clinical trials; teleconferences
2.  High-dimensional bolstered error estimation 
Bioinformatics  2011;27(21):3056-3064.
Motivation: In small-sample settings, bolstered error estimation has been shown to perform better than cross-validation and competitively with bootstrap with regard to various criteria. The key issue for bolstering performance is the variance setting for the bolstering kernel. Heretofore, this variance has been determined in a non-parametric manner from the data. Although bolstering based on this variance setting works well for small feature sets, results can deteriorate for high-dimensional feature spaces.
Results: This article computes an optimal kernel variance depending on the classification rule, sample size, model and feature space, both the original number and the number remaining after feature selection. A key point is that the optimal variance is robust relative to the model. This allows us to develop a method for selecting a suitable variance to use in real-world applications where the model is not known, but the other factors in determining the optimal kernel are known.
Availability: Companion website at
PMCID: PMC3198579  PMID: 21914630
3.  Progression-free Survival Decreases with Each Subsequent Therapy in Patients Presenting for Phase I Clinical Trials 
Journal of Cancer  2011;3:7-13.
Background: There is often a finite progression-free interval of time between one systemic therapy and the next when treating patients with advanced cancer. While it appears that progression-free survival (PFS) between systemic therapies tends to get shorter for a number of factors, there has not been a formal evaluation of diverse tumor types in an advanced cancer population treated with commercially-available systemic therapies.
Methods: In an attempt to clarify the relationship between PFS between subsequent systemic therapies, we analyzed the records of 165 advanced cancer patients coming to our clinic for consideration for participation in six different phase I clinical trials requiring detailed and extensive past medical treatment history documentation.
Results: There were 77 men and 65 women meeting inclusion criteria with a median age at diagnosis of 55.3 years (range 9.4-81.6). The most common cancer types were colorectal (13.9%), other gastrointestinal (11.8%), prostate (11.8%). A median of 3 (range 1-11) systemic therapies were received prior to phase I evaluation. There was a significant decrease in PFS in systemic therapy for advanced disease from treatment 1 to treatment 2 to treatment 3 (p = 0.002), as well as, from treatment 1 through treatment 5 (p < 0.001).
Conclusions: In an advanced cancer population of diverse tumor types, we observe a statistically significant decrease in PFS with each successive standard therapy. Identification of new therapies that reverse this trend of decreasing PFS may lead to improved clinical outcomes.
PMCID: PMC3245603  PMID: 22211140
Progression-free survival; chemotherapy; advanced cancer; systemic therapy; phase I clinical trials
4.  RNAi phenotype profiling of kinases identifies potential therapeutic targets in Ewing's sarcoma 
Molecular Cancer  2010;9:218.
Ewing's sarcomas are aggressive musculoskeletal tumors occurring most frequently in the long and flat bones as a solitary lesion mostly during the teen-age years of life. With current treatments, significant number of patients relapse and survival is poor for those with metastatic disease. As part of novel target discovery in Ewing's sarcoma, we applied RNAi mediated phenotypic profiling to identify kinase targets involved in growth and survival of Ewing's sarcoma cells.
Four Ewing's sarcoma cell lines TC-32, TC-71, SK-ES-1 and RD-ES were tested in high throughput-RNAi screens using a siRNA library targeting 572 kinases. Knockdown of 25 siRNAs reduced the growth of all four Ewing's sarcoma cell lines in replicate screens. Of these, 16 siRNA were specific and reduced proliferation of Ewing's sarcoma cells as compared to normal fibroblasts. Secondary validation and preliminary mechanistic studies highlighted the kinases STK10 and TNK2 as having important roles in growth and survival of Ewing's sarcoma cells. Furthermore, knockdown of STK10 and TNK2 by siRNA showed increased apoptosis.
In summary, RNAi-based phenotypic profiling proved to be a powerful gene target discovery strategy, leading to successful identification and validation of STK10 and TNK2 as two novel potential therapeutic targets for Ewing's sarcoma.
PMCID: PMC2933621  PMID: 20718987
5.  Inference of Gene Regulatory Networks Using Time-Series Data: A Survey 
Current Genomics  2009;10(6):416-429.
The advent of high-throughput technology like microarrays has provided the platform for studying how different cellular components work together, thus created an enormous interest in mathematically modeling biological network, particularly gene regulatory network (GRN). Of particular interest is the modeling and inference on time-series data, which capture a more thorough picture of the system than non-temporal data do. We have given an extensive review of methodologies that have been used on time-series data. In realizing that validation is an impartible part of the inference paradigm, we have also presented a discussion on the principles and challenges in performance evaluation of different methods. This survey gives a panoramic view on these topics, with anticipation that the readers will be inspired to improve and/or expand GRN inference and validation tool repository.
PMCID: PMC2766792  PMID: 20190956
6.  Performance of Feature Selection Methods 
Current Genomics  2009;10(6):365-374.
High-throughput biological technologies offer the promise of finding feature sets to serve as biomarkers for medical applications; however, the sheer number of potential features (genes, proteins, etc.) means that there needs to be massive feature selection, far greater than that envisioned in the classical literature. This paper considers performance analysis for feature-selection algorithms from two fundamental perspectives: How does the classification accuracy achieved with a selected feature set compare to the accuracy when the best feature set is used and what is the optimal number of features that should be used? The criteria manifest themselves in several issues that need to be considered when examining the efficacy of a feature-selection algorithm: (1) the correlation between the classifier errors for the selected feature set and the theoretically best feature set; (2) the regressions of the aforementioned errors upon one another; (3) the peaking phenomenon, that is, the effect of sample size on feature selection; and (4) the analysis of feature selection in the framework of high-dimensional models corresponding to high-throughput data.
PMCID: PMC2766788  PMID: 20190952

Results 1-6 (6)