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Overview of Omics Biomarker Discovery and Design Considerations for Biomarker-Informed Clinical Trials

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Biopharmaceutical Applied Statistics Symposium

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Abstract

Biomarkers have proven useful for understanding disease progression and treatment response in precision medicine development. Rapid development of high-throughput omics technologies has enabled scientists to discover new biomarkers with low cost. Leveraging available technology and biomarker information, novel biomarker -based clinical trial designs have been proposed and have proven beneficial to many clinical programs. In this chapter, we discuss the omics technologies and statistical issues that are related to biomarker discovery. We also provide an overview of current development of biomarker -enabled clinical trial designs.

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References

  • Albert, S. R., Sinicrope, F. A., & Grothey, A. (2005). N0147: A randomized phase III trial of oxaliplatin plus 5-fluorouracil/leucovorin with or without cetuximab after curative resection of stage III colon cancer. Clinical Colorectal Cancer, 5(3), 211–213.

    Article  Google Scholar 

  • Amado, R. G., Wolf, M., Peeters, M., Van Cutsem, E., Siena, S., Freeman, D., et al. (2008). Wild-type KRAS is required for panitumumab efficacy in patients with metastatic colorectal cancer. Journal of Clinical Oncology, 26(10), 1626–1634.

    Google Scholar 

  • Baker, S. G., & Freedman, L. S. (1995). Potential impact of genetic testing on cancer prevention trials, using breast cancer as an example. Journal of the National Cancer Institute, 87, 1137–1144.

    Article  Google Scholar 

  • Baker, S. G., & Kramer, B. S. (2005). Statistics for weighing benefits and harms in a proposed genetic substudy of a randomized cancer prevention trial. Applied Statistics, 54(5), 941–954.

    MathSciNet  MATH  Google Scholar 

  • Baker, S. G., Kramer, B. S., Sargent, D. J., & Bonetti, M. (2012). Biomarkers, subgroup evaluation, and clinical trial design. Discovery medicine, 13(70), 187–192.

    Google Scholar 

  • Barker, A. D., Sigman, C. C., Kelloff, G. J., Hylton, N. M., Berry, D. A., & Esserman, L. J. (2009). ISPY2: An adaptive breast cancer trial design in the setting of neoadjuvant chemotherapy. Clinical Pharmacology & Therapeutics, 86, 97–100.

    Article  Google Scholar 

  • Bauer, P. (1991). Multiple testing in clinical trials. Statistics in Medicine, 10, 871–890.

    Article  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(1), 289–300.

    MathSciNet  MATH  Google Scholar 

  • Bersanelli, M., Mosca, E., Remondini, D., Giampieri, E., Sala, C., Castellani, G., et al. (2016). Methods for the integration of multi-omics data: Mathematical aspects. BMC Bioinformatics, 17(Suppl 2), S15.

    Article  Google 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.

    Article  Google Scholar 

  • Bokemeyer, C., Bondarenko, I., Makhson, A., Hartmann, J. T., Aparicio, J., de Braud, F., et al. (2009). Fluorouracil, leucovorin, and oxaliplatin with and without cetuximab in the first-line treatment of metastatic colorectal cancer. Journal of Clinical Oncology, 27(5), 663–671.

    Google Scholar 

  • Bonetti, M., & Gelber, R. D. (2000). A graphical method to assess treatment-covariate interactions using the cox model on subsets of the data. Statistics in Medicine, 19, 2595–2609.

    Article  Google Scholar 

  • Bonetti, M., & Gelber, R. D. (2004). Patterns of treatment effects in subsets of patients in clinical trials. Biostatistics, 5, 465–481.

    Article  Google Scholar 

  • Cai, T., Tian, L., Wong, P. H., & Wei, L. J. (2011). Analysis of randomized comparative clinical trial data for personalized treatment selections. Biostatistics, 12(2), 270–282.

    Article  Google Scholar 

  • Chapman, P. B., Hauschild, A., Robert, C., Haanen, J. B., Ascierto, P., Larkin, J., et al. (2011). Improved survival with vemurafenib in melanoma with BRAF V600E mutation. New England Journal of Medicine, 364(26), 2507–2516.

    Google Scholar 

  • Christensen, J. G., et al. (2007). Cytoreductive antitumor activity of PF-2341066, a novel inhibitor of anaplastic lymphoma kinase and c-Met, in experimental models of anaplastic large-cell lymphoma. Molecular Cancer Therapeutics, 6, 3314–3322.

    Article  Google Scholar 

  • Cobo, M., Isla, D., Massuti, B., et al. (2007). Customizing cisplatin based on quantitative excision repair cross-complementing 1 mRNA expression: A phase III trial in non-small-cell lung cancer. Journal of Clinical Oncology, 25(19), 2747–2754.

    Article  Google Scholar 

  • Conley, B. A., & Doroshow, J. H. (2014). Molecular analysis for therapy choice: NCI MATCH. Seminars in Oncology, 41, 297–299.

    Article  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.

    Article  Google Scholar 

  • Freidlin, B., McShane, L. M., & Korn, E. L. (2010). Randomized clinical trials with biomarkers: Design issues. Journal of the National Cancer Institute, 102(3), 152–160.

    Article  Google Scholar 

  • Freidlin, B., Jiang, W., & Simon, R. (2009). The cross-validated adaptive signature design. Clinical Cancer Research, 16(2), 691–698.

    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.

    Google Scholar 

  • Janes, H., Pepe, M. S., Bossuyt, P. M., & Barlow, W. E. (2011). Measuring the performance of markers for guiding treatment decisions. Annals of Internal Medicine, 154(4), 253–259.

    Article  Google 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.

    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.

    Article  MathSciNet  Google Scholar 

  • Jiang, W., Freidlin, B., & Simon, R. (2007). Biomarker-adaptive threshold design: A procedure for evaluating treatment with possible biomarker-defined subset effect. Journal of the National Cancer Institute, 99(13), 1036–1043.

    Google Scholar 

  • Johnstone, I., & Titterington, D. (2009). Statistical challenges of high-dimensional data. Philosophical Transactions of the Royal Society A, 367, 4237–4253.

    Article  MathSciNet  Google Scholar 

  • Jung, S., & Young, S. (2012). Power and sample size calculation for microarray studies. Journal of Biopharmaceutical Statistics, 22(1), 30–42.

    Article  MathSciNet  Google Scholar 

  • Kaplan, R., Maughan, T., Crook, A., Fisher, D., Wilson, R., Brown, L., et al. (2007). Evaluating many treatments and biomarkers in oncology: A new design. Journal of Clinical Oncology, 31, 4562–4568.

    Article  Google Scholar 

  • Karapetis, C. S., Khambata-Ford, S., Jonker, D. J., O’Callaghan, C. J., Tu, D., Tebbutt, N. C., et al. (2008). K-ras mutations and benefit from cetuximab in advanced colorectal cancer. New England Journal of Medicine, 359(17), 1757–1765.

    Google Scholar 

  • Kim, E. S., Herbst, R. S., Wistuba, I. I., Lee, J. J., Blumenschein, G. R., Jr., Tsao, A., et al. (2011). The BATTLE trial: Personalizing therapy for lung cancer. Cancer Discovery, 1, 44–53.

    Article  Google Scholar 

  • King, M. C., Wieand, S., Hale, K., Lee, M., Walsh, T., Owens, K., et al.; National Surgical Adjuvant Breast and Bowel Project. (2001). Tamoxifen and breast cancer incidence among women with inherited mutations in BRCA1 and BRCA2: National surgical adjuvant breast and bowel project (NSABP-P1) breast cancer prevention trial. JAMA, 286(18), 2251–2256.

    Google Scholar 

  • Klein, R. J. (2007). Power analysis for genome-wide association studies. BMC Genetics, 8, 58.

    Article  Google Scholar 

  • Kwak, E. L., et al. (2010). Anaplastic lymphoma kinase inhibition in non–small-cell lung cancer. New England Journal of Medicine, 363, 1693–1703.

    Article  Google Scholar 

  • Lee, J., & Liu, D. (2008). A predictive probability design for phase II cancer clinical trials. Clinical Trials, 5(2), 93–106.

    Article  Google Scholar 

  • Lee, S., Wu, M. C., & Lin, X. (2012). Optimal tests for rare variant effects in sequencing association studies. Biostatistics, 13, 762–775.

    Article  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, 23(6), 739–746.

    Article  Google Scholar 

  • Lowe, W. L., & Reddy, T. E. (2015). Genomic approaches for understanding the genetics of complex disease. Genome Research, 25, 1432–1441.

    Article  Google Scholar 

  • Mandrekar, S. J., & Sargent, D. (2009). Clinical trial designs for predictive biomarker validation: Theoretical considerations and practical challenges. Journal of Clinical Oncology, 27(24), 4027–4034.

    Article  Google Scholar 

  • Mehta, C., Gao, P., Bhatt, D. L., Harrington, R. A., Skerjanec, S., & Ware, J. H. (2009). Optimizing trial design. 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.

    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. International Journal of Epidemiology, 41(1), 273–286.

    Article  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.

    Article  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.

    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).

    Google Scholar 

  • Romond, E. H., Perez, E. A., Bryant, J., Suman, V. J., Geyer, C. E. Jr, Davidson, N. E., et al. (2005). Trastuzumab plus adjuvant chemotherapy for operable HER2-positive breast cancer. New England Journal of Medicine, 353(16), 1673–1684.

    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.

    Article  Google Scholar 

  • Sargent, D. J., Conley, B. A., Allegra, C., & Collete, L. (2005). Clinical trial designs for predictive marker validation in cancer treatment trials. Journal of Clinical Oncology, 23(9), 2020–2227.

    Article  Google Scholar 

  • Selaru, P., Tang, Y., Huang, B., Polli, A., Wilner, K., Donnelly, E., et al. (2016). Sufficiency of single-arm studies to support registration of targeted agents in molecularly selected patients with cancer: Lessons from the clinical development of Crizotinib. Clinical and Translational Science.

    Google Scholar 

  • Shabalin, A., Tjelmeland, H., Fan, C., Perou, C., & Nobel, A. (2008). Merging two gene-expression studies via cross-platform normalization. Bioinformatics, 24(9), 1154–1160.

    Article  Google Scholar 

  • Shaw, A. T., et al. (2014). Crizotinib in ROS1-rearranged non-small-cell lung cancer. New England Journal of Medicine, 371, 1963–1971.

    Article  Google Scholar 

  • Shyr, D., & Liu, Q. (2013). Next generation sequencing in cancer research and clinical application. Biological Procedures Online, 15, 4.

    Article  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.

    Article  Google Scholar 

  • Song, X., & Pepe, M. S. (2004). Evaluating markers for selecting a patient’s treatment. Biometrics, 60(4), 874–883.

    Article  MathSciNet  Google Scholar 

  • Song, Y., & Chi, G. Y. (2007). A method for testing a prespecified subgroup in clinical trials. Statistics in Medicine, 26(19), 3535–3549.

    Article  MathSciNet  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.

    Article  Google Scholar 

  • Storey, J. D. (2002). A direct approach to false discovery rates. Journal of the Royal Statistical Society, 64, 479–498.

    Article  MathSciNet  Google Scholar 

  • Stylianou, S., Clarke, R., & Brennan, K. (2006). Aberrant activation of notch signaling in human breast cancer. Cancer Research, 66(3), 1517–1525.

    Article  Google Scholar 

  • Thompson, J., Tan, J., & Greene, C. (2016). Cross-platform normalization of microarray and RNA-seq data for machine learning applications. Peer J, 4, e1621.

    Article  Google Scholar 

  • Van Cutsem, E., Köhne, C. H., Hitre, E., Zaluski, J., Chang Chien, C. R., Makhson, A., et al. (2009). Cetuximab and chemotherapy as initial treatment for metastatic colorectal cancer. New England Journal of Medicine, 360(14), 1408–1417.

    Google Scholar 

  • Wang S. J., O’Neill R. T., & Hung, H. M. (2007). Approaches to evaluation of treatment effect in randomized clinical trials with genomic subset. Pharmaceutical Statistics, 6(3), 227–244.

    Google Scholar 

  • Wetterstrand, K. A. (2016). DNA sequencing costs: Data from the NHGRI genome sequencing program (GSP). Retrieved 23 December 2016, from www.genome.gov/sequencingcostsdata.

  • Wu, Z., & Zhao, H. (2009). Statistical power of model selection strategies for genome-wide association studies. PLoS Genetics, 5, e1000582.

    Article  Google Scholar 

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Authors and Affiliations

  1. Global Product Development, Pfizer Inc., 3rd FL, 300 Technology Square, Cambridge, MA, 02139, USA

    Weidong Zhang & Jing Wang

  2. Global Product Development, Pfizer Inc., 280 Shennecossett Rd, Groton, CT, 06340, USA

    Bo Huang

  3. Worldwide Research and Development, Pfizer Inc., 1 Portland Street, Cambridge, MA, 02139, USA

    Sandeep Menon

Authors
  1. Weidong Zhang
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  2. Bo Huang
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  3. Jing Wang
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  4. Sandeep Menon
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Correspondence to Weidong Zhang .

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Editors and Affiliations

  1. Jiann-Ping Hsu College of Public Health, Georgia Southern University, Statesboro, GA, USA

    Karl E. Peace

  2. School of Social Work and Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA

    Ding-Geng Chen

  3. Boston University, Cambridge, MA, USA

    Sandeep Menon

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Zhang, W., Huang, B., Wang, J., Menon, S. (2018). Overview of Omics Biomarker Discovery and Design Considerations for Biomarker-Informed Clinical Trials. In: Peace, K., Chen, DG., Menon, S. (eds) Biopharmaceutical Applied Statistics Symposium . ICSA Book Series in Statistics. Springer, Singapore. https://doi.org/10.1007/978-981-10-7820-0_2

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