Advancing cancer drug development through precision medicine and innovative designs

References

• Allen, G. (2017). Statistical data integration: Challenges and opportunities. Statistical Modelling 17(4–5):332–337.
   Web of Science ®Google Scholar
• Benjamini, Y., Hochberg, Y. (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society 57(No. 1):pp. 289-300.
   Google Scholar
• Bersanelli, M., Mosca, E., Remondini, D., Giampieri, E., Sala, C., Castellani, G., Milanesi, L. (2016). Methods for the integration of multi-omics data: Mathematical aspects. BMC Bioinformatics 17(Suppl 2):S15.
   PubMedGoogle Scholar
• Bi, R., Liu, P. (2016). Sample size calculation while controlling false discovery rate for differential expression analysis with RNA-sequencing experiments. BMC Bioinformatics 17:146.
   PubMed Web of Science ®Google Scholar
• Biomarkers Definition Working Group. (2001). Biomarkers and surrogate endpoints: Preferred definitions and conceptual framework. Clin Pharmacol Therapeutics 69:89–95.
   PubMed Web of Science ®Google Scholar
• Breiman, L., Friedman, J., Olshen, R., Stone, C. (1984). Classification and Regression Trees. New York, NY: Chapman & Hall.
   Google Scholar
• Chapman, P. B., Hauschild, A., Robert, C., Haanen, J. B., Ascierto, P., Larkin, J., … McArthur, G. A. (2011). Improved survival with vemurafenib in melanoma with BRAF V600E mutation. New England Journal of Medicine 364(26):2507–2516.
   PubMed Web of Science ®Google Scholar
• Chen, C., Li, N., Shentu, Y., Pang, L., Beckman, R. A. (2016). Adaptive informational design of confirmatory phase iii trials with an uncertain biomarker effect to improve the probability of success. Statistics in Biopharmaceutical Research 10.1080/19466315.2016.1173582.
   PubMed Web of Science ®Google Scholar
• Collins, F. S., Varmus, H. (2015). A new initiative on precision medicine. The New England Journal of Medicine 372(9):793–795.
   PubMed Web of Science ®Google Scholar
• Dolsten, M., Søgaard, M. (2012). Precision medicine: An approach to R&D for delivering superior medicines to patients. Clinical and Translational Medicine 1:7.
   PubMedGoogle Scholar
• European Medicines Agency (EMA). Giotrib (afatinib). EMA http://www.ema.europa.eu/ema/index.jsp?curl=pages/medicines/human/medicines/002280/human_med_001698.jsp&mid=WC0b01ac058001d124 (2016).
   Google Scholar
• Fadista, J., Manning, A., Florez, J., Groop, L. (2016). The (in)famous GWAS P-value threshold revisited and updated for low-frequency variants. European Journal of Human Genetics 24:1202–1205.
   PubMed Web of Science ®Google Scholar
• Fleming, T. R., DeMets, D. L. (1996). Surrogate end points in clinical trials: Are we being misled? Annals of Internal Medicine 125(7):605–613.
   PubMed Web of Science ®Google Scholar
• Freidlin, B., Jiang, W., Simon, R. (2010). The cross-validated adaptive signature design. Clin Cancer Res 16(2):691–698.
   PubMed Web of Science ®Google Scholar
• Freidlin, B., Korn, E. L., Gray, R. (2014). Marker sequential test (MaST) design. Clinical Trials 11(1):19–27.
   PubMed Web of Science ®Google Scholar
• Friedman, J., Hastie, T., Tibshirani, R. (2010). Regularization paths for generalized linear models via coordinate descent. Journal of Statistical Software 33:1–22.
   PubMed Web of Science ®Google Scholar
• George, S., Wang, X. (2012). Targeted Clinical Trials. D. Harrington. ( eds)., Designs for Clinical Trials. Applied Bioinformatics and Biostatistics in Cancer Research. Springer:New York, NY.
   Google Scholar
• Gosho, M., Nagashima, K., Sato, Y. (2012). Study designs and statistical analyses for biomarker research. Sensors 12:8966–8986.
   PubMed Web of Science ®Google Scholar
• Hong, E. P., Park, J. W. (2012). Sample size and statistical power calculation in genetic association studies. Genomics & Informatics 10(2):117–122.
   PubMedGoogle Scholar
• Jenkins, M., Stone, A., Jennison, C. (2011). An adaptive seamless phase II/III design for oncology trials with subpopulation selection using correlated survival endpoint. Pharmaceutical Statistics 10(4):347–356.
   PubMed Web of Science ®Google Scholar
• Jiang, W., Yu, W. (2016). Power estimation and sample size determination for replication studies of genome-wide association studies. BMC Genomics 17(Suppl 1):3.
   PubMedGoogle Scholar
• Johnstone, I., Titterington, D. (2009). Statistical challenges of high-dimensional data. Phil Transactions R Social A 367:4237–4253.
   PubMed Web of Science ®Google Scholar
• Jung, S. (2013). Sample size calculation for microarray studies with survival endpoints. Journal of Computer Science and Systems Biology 2013(6):3.
   Google Scholar
• Jung, S., Young, S. (2012). Power and sample size calculation for microarray studies. Journal of Biopharmaceutical Statistics 22(1):30–42.
   PubMed Web of Science ®Google Scholar
• Katz, R. (2004). Biomarkers and surrogate Markers: An FDA perspective. NeuroRx : the Journal of the American Society for Experimental NeuroTherapeutics 1(2):189–195.
   PubMedGoogle Scholar
• Klein, R. J. (2007). Power analysis for genome-wide association studies. BMC Genetics 8:58.
   PubMed Web of Science ®Google Scholar
• Lee, S., Wu, M. C., Lin, X. (2012). Optimal tests for rare variant effects in sequencing association studies. Biostatistics 13:762–775.
   PubMed Web of Science ®Google Scholar
• Liaw, A., Wiener, M. (2002). Classification and regression by randomForest. R News 2(3):18–22.
   Google Scholar
• Liu, P., Hwang, J. T. G. (2007). Quick calculation for sample size while controlling false discovery rate with application to microarray analysis. Bioinformatics (Oxford, England) 23(6):739–746.
   PubMed Web of Science ®Google Scholar
• Lowe, W. L., Reddy, T. E. (2015). Genomic approaches for understanding the genetics of complex disease. Genome Research 25:1432–1441.
   PubMed Web of Science ®Google Scholar
• Mehta, C., Gao, P., Bhatt, D., Harrington, R., Skerjanec, S., Ware, J. (2009). Optimizing Trial Design: Sequential, Adaptive, and Enrichment Strategies, Circulation 119:597–605.
   Google Scholar
• Meienberg, J., Bruggmann, R., Oexle, K., Matyas, G. (2016). Clinical sequencing. Is WGS the Better WES? Human Genetics 135:359–362.
   PubMed Web of Science ®Google Scholar
• Morgan, P., Van Der Graaf, P. H., Arrowsmith, J., Feltner, D. E., Drummond, K. S., Wegner, C. D., Street, S. D. (2012). Can the flow of medicines be improved? Fundamental pharmacokinetic and pharmacological principles toward improving Phase II survival. Drug Discov Today 17:419–424.
   PubMed Web of Science ®Google Scholar
• Paik, S., Shak, S., Tang, G., Kim, C., Baker, J., Cronin, M. et al. (2004). A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. The New England Journal of Medicine 35127:2817–2826.
   PubMed Web of Science ®Google Scholar
• Panagiotou, O. A., Ioannidis, J. P. (2012). Genome-wide significance project. What Should the Genome-Wide Significance Threshold Be? Empirical Replication of Borderline Genetic Associations. Int J Epidemiol. 41(1):273–286.
   Google Scholar
• Park, J. H., Wacholder, S., Gail, M. H., Peters, U., Jacobs, K. B., Chanock, S. J. et al. (2010). Estimation of effect size distribution from genome-wide association studies and implications for future discoveries. Nature Genetics 42:570–575.
   PubMed Web of Science ®Google Scholar
• Pe’er, I., Yelensky, R., Altshuler, D., Daly, M. (2008). Estimation of the multiple testing burden for genomewide association studies of nearly all common variants. Genetic Epidemiology 32:381–385.
   PubMed Web of Science ®Google Scholar
• Pineda, S., Real, F. X., Kogevinas, M., Carrato, A., Chanock, S. J., Malats, N. et al. (2015). Integration analysis of three omics data using penalized regression methods: An application to bladder cancer. PLoS Genetics 11:12.
   Web of Science ®Google Scholar
• Saccenti, E., Timmerman, M. E. (2016). Approaches to sample size determination for multivariate data: Applications to pca and pls-da of omics data. Journal of Proteome Research 15:2379–2393.
   PubMed Web of Science ®Google Scholar
• Shabalin, A., Tjelmeland, H., Fan, C., Perou, C., Nobel, A. (2008). Merging two gene-expression studies via cross-platform normalization. Bioinformatics (Oxford, England) 24(9):1154–1160.
   PubMed Web of Science ®Google Scholar
• Spencer, C. C., Su, Z., Donnelly, P., Marchini, J. (2009). Designing genome-wide association studies: Sample size, power, imputation, and the choice of genotyping chip. PLoS Genetics 5:e1000477.
   PubMed Web of Science ®Google Scholar
• Shyr, D., Liu, Q. (2013). Next generation sequencing in cancer research and clinical application. Biological Procedures Online 15(1):4.
   PubMedGoogle Scholar
• Simon, R., Maitournam, A. (2004). Evaluating the efficiency of targeted designs for randomized clinical trials. Clinical Cancer Research 20:6759–6763.
   Google Scholar
• Simon, R., Wang, S. J. (2006). Use of genomic signatures in therapeutics development in oncology and other diseases. The Pharmacogenomics Journal 6(3):166–173.
   PubMed Web of Science ®Google Scholar
• Storey, J. D. (2002). A direct approach to false discovery rates. Journal of the Royal Statistical Society 64:479–498.
   Google Scholar
• Teng, Z., Liu, Y., Wang, J., Hua, Z., Chang, M. (2016). Oncology Trials: Development strategy. To Appear in the 4th Edition of Encyclopedia of Biopharmaceutical Statistics. Boca Raton, FL: CRC Press.
   Google Scholar
• TESARO. (2016). TESARO’s Niraparib Significantly Improved Progression-Free Survival for Patients With Ovarian Cancer in Both Cohorts of the Phase 3 NOVA Trial. http://ir.tesarobio.com/news-releases/news-release-details/tesaros-niraparib-significantly-improvedprogression-free
   Google Scholar
• Thomas, D.W., Burns, J., Audette, J., Carroll, A., Dow-Hygelund, C., Hay, M. (2016). Clinical Development Success Rates 2006–2015. June 2016. Available at: https://www.bio.org/sites/default/files/Clinical%20Development%20Success%20Rates%202006-2015%20-%20BIO,%20Biomedtracker,%20Amplion%202016.pdf
   Google Scholar
• Thompson, J., Tan, J., Greene, C. (2016). Cross-platform normalization of microarray and RNA-seq data for machine learning applications. Peer Journal 4:e1621.
   PubMed Web of Science ®Google Scholar
• US Food and Drug Administration (FDA). FDA approves afatinib. FDA http://www.fda.gov/Drugs/InformationOnDrugs/ApprovedDrugs/ucm360574. htm (2016). 18. Lindeman, N. I.
   Google Scholar
• Wang, J., Chow, S., Chang, M. (2016). Biomarker: Driven adaptive design for precision medicine. Journal Translational Biomarkers Diagnosis 2(1):15–24.
   Google Scholar
• Wang, S., O’Neill, R., Hung, H. (2007). Approaches to evaluation of treatment effect in randomized clinical trials with genomic subset. Pharmaceutical Statistics 6(3):227–244.
   PubMed Web of Science ®Google Scholar
• Wu, Z., Zhao, H. (2009). Statistical power of model selection strategies for genome-wide association studies. PLoS Genetics 5:e1000582.
   PubMed Web of Science ®Google Scholar
• Yang, B., Zhou, Y., Zhang, L., Cui, L. (2015). Enrichment design with patient population augmentation. Contemporary Clinical Trials 42:60–67.
   PubMedGoogle Scholar
• Wetterstrand, K. A. (2016). DNA Sequencing Costs: Data from the NHGRI Genome Sequencing Program (GSP) Available at: www.genome.gov/sequencingcostsdata. Accessed 12/ 23/2016
   Google Scholar