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1.  Estimation of α/β ratio of prostate cancer using post-treatment PSA repeated measures: a pooled analysis over 6 cohorts of patients treated by external beam radiation therapy 
To estimate the ratio α/β of Prostate Cancer from the effect of dose of radiation on the long term rise of prostate specific antigen (PSA).
Materials and methods
Repeated measures of PSA from 5,093 patients treated for localized prostate cancer by external beam radiation therapy (EBRT) were analysed. Patients came from 6 large cohorts. A biphasic linear mixed model described the post-treatment evolution of PSA. The effect of the radiation dose schedule on the long term rate of rise of PSA was estimated from the model. The model adjusted for standard prognostic factors (T-stage, initial PSA and Gleason) and cohort specific effects.
Adjusted for other factors, total dose of EBRT and sum of squared doses per fraction were associated with long term rate of change of PSA (respectively p=0.0017 and p=0.0003), an increase of each being associated with a lower rate of rise. The ratio α/β was estimated at 1.55 Gy with 95% confidence interval [0.46;4.52]. This estimate was robust to adjustment of the linear mixed model but varied according to which cohorts were included, especially the one bringing hypofractioned schemes.
Using more than 5,000 patients treated by EBRT and a method that accounts for all the repeated measures of PSA after end of treatment rather than only the time of biochemical recurrence, a very low and precise value for α/β was estimated. This result favors hypofractionated radiation therapy that could better control the tumor with a reduced late toxicity. However outcome data from EBRT studies using higher doses per fraction are still needed to validate this result.
PMCID: PMC4122313  PMID: 20381268
Prostate cancer; Prostate-specific Antigen; Radiation therapy; Alpha/beta; Radiosensitivity
The annals of applied statistics  2013;7(4):2272-2292.
Motivated by the increasing use of and rapid changes in array technologies, we consider the prediction problem of fitting a linear regression relating a continuous outcome Y to a large number of covariates X, eg measurements from current, state-of-the-art technology. For most of the samples, only the outcome Y and surrogate covariates, W, are available. These surrogates may be data from prior studies using older technologies. Owing to the dimension of the problem and the large fraction of missing information, a critical issue is appropriate shrinkage of model parameters for an optimal bias-variance tradeoff. We discuss a variety of fully Bayesian and Empirical Bayes algorithms which account for uncertainty in the missing data and adaptively shrink parameter estimates for superior prediction. These methods are evaluated via a comprehensive simulation study. In addition, we apply our methods to a lung cancer dataset, predicting survival time (Y) using qRT-PCR (X) and microarray (W) measurements.
PMCID: PMC3891514  PMID: 24436727
High-dimensional data; Markov chain Monte Carlo; missing data; measurement error; shrinkage
3.  Criteria for the use of omics-based predictors in clinical trials: explanation and elaboration 
BMC Medicine  2013;11:220.
High-throughput ?omics? technologies that generate molecular profiles for biospecimens have been extensively used in preclinical studies to reveal molecular subtypes and elucidate the biological mechanisms of disease, and in retrospective studies on clinical specimens to develop mathematical models to predict clinical endpoints. Nevertheless, the translation of these technologies into clinical tests that are useful for guiding management decisions for patients has been relatively slow. It can be difficult to determine when the body of evidence for an omics-based test is sufficiently comprehensive and reliable to support claims that it is ready for clinical use, or even that it is ready for definitive evaluation in a clinical trial in which it may be used to direct patient therapy. Reasons for this difficulty include the exploratory and retrospective nature of many of these studies, the complexity of these assays and their application to clinical specimens, and the many potential pitfalls inherent in the development of mathematical predictor models from the very high-dimensional data generated by these omics technologies. Here we present a checklist of criteria to consider when evaluating the body of evidence supporting the clinical use of a predictor to guide patient therapy. Included are issues pertaining to specimen and assay requirements, the soundness of the process for developing predictor models, expectations regarding clinical study design and conduct, and attention to regulatory, ethical, and legal issues. The proposed checklist should serve as a useful guide to investigators preparing proposals for studies involving the use of omics-based tests. The US National Cancer Institute plans to refer to these guidelines for review of proposals for studies involving omics tests, and it is hoped that other sponsors will adopt the checklist as well.
PMCID: PMC3852338  PMID: 24228635
Analytical validation; Biomarker; Diagnostic test; Genomic classifier; Model validation; Molecular profile; Omics; Personalized medicine; Precision Medicine; Treatment selection
4.  Development and Validation of a qRT-PCR Classifier for Lung Cancer Prognosis 
This prospective study aimed to develop a robust and clinically-applicable method to identify high-risk early stage lung cancer patients and then to validate this method for use in future translational studies.
Patients and Methods
Three published Affymetrix microarray data sets representing 680 primary tumors were used in the survival-related gene selection procedure using clustering, Cox model and random survival forest (RSF) analysis. A final set of 91 genes was selected and tested as a predictor of survival using a qRT-PCR-based assay utilizing an independent cohort of 101 lung adenocarcinomas.
The RSF model built from 91 genes in the training set predicted patient survival in an independent cohort of 101 lung adenocarcinomas, with a prediction error rate of 26.6%. The mortality risk index (MRI) was significantly related to survival (Cox model p < 0.00001) and separated all patients into low, medium, and high-risk groups (HR = 1.00, 2.82, 4.42). The MRI was also related to survival in stage 1 patients (Cox model p = 0.001), separating patients into low, medium, and high-risk groups (HR = 1.00, 3.29, 3.77).
The development and validation of this robust qRT-PCR platform allows prediction of patient survival with early stage lung cancer. Utilization will now allow investigators to evaluate it prospectively by incorporation into new clinical trials with the goal of personalized treatment of lung cancer patients and improving patient survival.
PMCID: PMC3167380  PMID: 21792073
Lung cancer; qRT-PCR; Prognosis
5.  Gene Expression Patterns in Mismatch Repair-Deficient Colorectal Cancers Highlight the Potential Therapeutic Role of Inhibitors of the PI3K-AKT-mTOR pathway 
High-frequency microsatellite instable (MSI-H) tumors account for roughly 15% of colorectal cancers (CRC). Therapeutic decisions for CRC are empirically based and currently do not emphasize molecular subclassification despite of the increasing collection of gene expression information. Our objective was to identify low molecular weight compounds with preferential activity against MSI CRCs using combined gene expression data sets.
Experimental Design
Three expression/query signatures (discovery data set) characterizing MSI-H CRC were matched with information derived from changes induced in cell lines by 164 compounds, using the systems biology tool “Connectivity Map”. A series of sequential filtering and ranking algorithms were used to select the candidate compounds. Compounds were validated using two additional expression/query signatures (validation data set). Cytotoxic, cell cycle and apoptosis effects of validated compounds were evaluated in a panel of cell lines.
Fourteen of the 164 compounds were validated as targeting MSI-H cells lines using the bioinformatics approach; Rapamycin, LY-294002, 17-AAG and Trichostatin-A were the most robust candidate compounds. In vitro results showed that MSI-H cell lines due to hypermethylation of MLH1 are preferentially targeted by Rapamycin (18.3 vs 4.4 μM, P=0.0824) and LY-294002 (15.02 vs 10.37 μM, P=0.0385) when compared to MSS cells. Preferential activity was also observed in MSH2 and MSH6-mutant cells.
Our study demonstrates that the PI3K-AKT-mTOR pathway is of special relevance in mismatch repair-deficient CRC. In addition, we show that amalgamation of gene expression information across studies provides a robust approach for selection of potential therapies corresponding to specific groups of patients.
PMCID: PMC3425357  PMID: 19351759
Microsatellite instability; colorectal cancer; gene expression patterns; rapamycin; mTOR pathway; PI3K inhibitors
6.  Comparison of seven methods for producing Affymetrix expression scores based on False Discovery Rates in disease profiling data 
BMC Bioinformatics  2005;6:26.
A critical step in processing oligonucleotide microarray data is combining the information in multiple probes to produce a single number that best captures the expression level of a RNA transcript. Several systematic studies comparing multiple methods for array processing have used tightly controlled calibration data sets as the basis for comparison. Here we compare performances for seven processing methods using two data sets originally collected for disease profiling studies. An emphasis is placed on understanding sensitivity for detecting differentially expressed genes in terms of two key statistical determinants: test statistic variability for non-differentially expressed genes, and test statistic size for truly differentially expressed genes.
In the two data sets considered here, up to seven-fold variation across the processing methods was found in the number of genes detected at a given false discovery rate (FDR). The best performing methods called up to 90% of the same genes differentially expressed, had less variable test statistics under randomization, and had a greater number of large test statistics in the experimental data. Poor performance of one method was directly tied to a tendency to produce highly variable test statistic values under randomization. Based on an overall measure of performance, two of the seven methods (Dchip and a trimmed mean approach) are superior in the two data sets considered here. Two other methods (MAS5 and GCRMA-EB) are inferior, while results for the other three methods are mixed.
Choice of processing method has a major impact on differential expression analysis of microarray data. Previously reported performance analyses using tightly controlled calibration data sets are not highly consistent with results reported here using data from human tissue samples. Performance of array processing methods in disease profiling and other realistic biological studies should be given greater consideration when comparing Affymetrix processing methods.
PMCID: PMC550659  PMID: 15705192

Results 1-6 (6)