You can also find a related feature on BDD's sister website. The journal watch editions posted there are more narrowly focused on therapeutic targets in oncology drug development (i.e., PI3K, ALK, HSP90, HER2, EGFR).
Ferté C, Fernandez M, Hollebecque A, Koscielny S, Levy A, Massard C, Balheda R, Bot B, Gomez-Roca C, Dromain C, Ammari S, Soria JC.Clin Cancer Res. 2014 Jan 1;20(1):246-52. doi: 10.1158/1078-0432.CCR-13-2098. Epub 2013 Nov 15.
PURPOSE: Response Evaluation Criteria in Solid Tumors (RECIST) evaluation does not take into account the pretreatment tumor kinetics and may provide incomplete information about experimental drug activity. Tumor growth rate (TGR) allows for a dynamic and quantitative assessment of the tumor kinetics. How TGR varies along the introduction of experimental therapeutics and is associated with outcome in phase I patients remains unknown.
EXPERIMENTAL DESIGN: Medical records from all patients (N = 253) prospectively treated in 20 phase I trials were analyzed. TGR was computed during the pretreatment period (reference) and the experimental period. Associations between TGR, standard prognostic scores [Royal Marsden Hospital (RMH) score], and outcome [progression-free survival (PFS) and overall survival (OS)] were computed (multivariate analysis).
RESULTS: We observed a reduction of TGR between the reference versus experimental periods (38% vs. 4.4%; P < 0.00001). Although most patients were classified as stable disease (65%) or progressive disease (25%) by RECIST at the first evaluation, 82% and 65% of them exhibited a decrease in TGR, respectively. In a multivariate analysis, only the decrease of TGR was associated with PFS (P = 0.004), whereas the RMH score was the only variable associated with OS (P = 0.0008). Only the investigated regimens delivered were associated with a decrease of TGR (P < 0.00001, multivariate analysis). Computing TGR profiles across different clinical trials reveals specific patterns of antitumor activity.
CONCLUSIONS: Exploring TGR in phase I patients is simple and provides clinically relevant information: (i) an early and subtle assessment of signs of antitumor activity; (ii) independent association with PFS; and (iii) it reveals drug-specific profiles, suggesting potential utility for guiding the further development of the investigational drugs.
Concordance Study of 3 Direct-to-Consumer Genetic-Testing Services
Kenta Imai, Larry J. Kricka and Paolo Fortina
BACKGROUND: Several companies offer direct-to-consumer (DTC) genetic testing to evaluate ancestry and wellness. Massive-scale testing of thousands of single-nucleotide polymorphisms (SNPs) is not error free, and such errors could translate into misclassification of risk and produce a false sense of security or unnecessary anxiety in an individual. We evaluated 3 DTC services and a genomics service that are based on DNA microarray or solution genotyping with hydrolysis probes (TaqMan® analysis) and compared the test results obtained for the same individual.
METHODS: We evaluated the results from 3 DTC services (23andMe, deCODEme, Navigenics) and a genomics-analysis service (Expression Analysis).
RESULTS: The concordance rates between the services for SNP data were >99.6%; however, there were some marked differences in the relative disease risks assigned by the DTC services (e.g., for rheumatoid arthritis, the range of relative risk was 0.9–1.85). A possible reason for this difference is that different SNPs were used to calculate risk for the same disease. The reference population also had an influence on the relative disease risk.
CONCLUSIONS: Our study revealed excellent concordance between the results of SNP analyses obtained from different companies with different platforms, but we noted a disparity in the data for risk, owing to both differences in the SNPs used in the calculation and the reference population used. The larger issues of the utility of the information and the need for risk data that match the user's ethnicity remain, however.
A dirty dozen: twelve p-value misconceptions.
The P value is a measure of statistical evidence that appears in virtually all medical research papers. Its interpretation is made extraordinarily difficult because it is not part of any formal system of statistical inference. As a result, the P value's inferential meaning is widely and often wildly misconstrued, a fact that has been pointed out in innumerable papers and books appearing since at least the 1940s. This commentary reviews a dozen of these common misinterpretations and explains why each is wrong. It also reviews the possible consequences of these improper understandings or representations of its meaning. Finally, it contrasts the P value with its Bayesian counterpart, the Bayes' factor, which has virtually all of the desirable properties of an evidential measure that the P value lacks, most notably interpretability. The most serious consequence of this array of P-value misconceptions is the false belief that the probability of a conclusion being in error can be calculated from the data in a single experiment without reference to external evidence or the plausibility of the underlying mechanism.
Semin Hematol. 2008 Jul;45(3):135-40. doi: 10.1053/j.seminhematol.2008.04.003.
Codevelopment of Genome-Based Therapeutics and Companion Diagnostics Insights From an Institute of Medicine Roundtable
Robert T. McCormack, PhD1; Joanne Armstrong, MD, MPH2; Debra Leonard, MD, PhD
JAMA. Published online February 12, 2014. doi:10.1001/jama.2014.1508
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