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Introduction to Clinical Trials, Clinical Trial Designs, and Statistical Terminology Used for Predictive Biomarker Research and Validation

  • Karla V. Ballman
Chapter

Abstract

This chapter provides an introduction to clinical trial designs and analysis techniques used in evaluating new experimental drugs for cancer patients. It also provides an overview of two major types of biomarkers, prognostic and predictive, that are commonly used in oncology. The chapter closes with descriptions of different clinical trial designs that incorporate, or discover, potential biomarkers.

Keywords

Clinical trial designs Methods for survival analysis Prognostic biomarker Predictive biomarker Biomarker clinical trial designs 

Notes

Glossary

Adjusted (or multivariable hazards ratio (HR)

A multivariable Cox model allows the evaluation of the association of multiple variables on the outcome (e.g., survival). This allows a more accurate assessment of the relationship of a variable of interest to overall survival by accounting for other variables that may be associated with survival. For example, when evaluating the association of a biomarker with survival, a treatment variable may be added to the model. This would allow the evaluation of the association of the biomarker with survival after accounting for the association of treatment with survival. The hazard ratio for a variable from a multivariable Cox model is referred to as a multivariable HR or an adjusted HR.

Continuous (bio)marker

A continuous biomarker is one that has an infinite number of possibilities; in other words, it can take on any value between its minimum and maximum value if it could be measured to any desired degree of precision. An example of a continuous biomarker is PSA level for prostate cancer. The minimum value is 0 and there is no absolute maximum. If PSA could be measured to any desired degree of precision, all nonnegative values are possible.

Cox proportional hazards model

A Cox proportional hazards model is a regression technique for time-to-event data (e.g., survival) where there is censoring (when some patients are alive at the time of analysis). It is a way to evaluate the association of a variable with the time-to-event outcome such as survival. The method is semi-parametric; that is, it does not assume a model for t survival but does assume that the effect of a variable on survival is constant over time. The association is measured by a hazard ratio (HR) where HR = 1 means no association, a HR <1 means increasing values of the variable reduces the chance of death, and HR >1 means that increasing value of the variable increases the chance of death.

Dichotomous (bio)marker

A dichotomous biomarker is one that takes one of two possible values. It is used to split patient cohorts into two categories or groups. An example of a dichotomous biomarker is estrogen receptor (ER) status for women with breast cancer: ER positive versus ER negative.

Log-rank test

A log-rank test is used to compare the survival distributions of two or more groups. The null hypothesis is that there is no difference among the groups. If the p-value is significant (e.g., less than 0.05), this is evidence that the groups have different survival experiences. Note this is only a test for a difference among the survival experiences and does not provide an estimate regarding the size of the differences between any two groups.

Meta-analysis

A meta-analysis encompasses techniques for combining data from multiple studies. An underlying assumption is that the treatment effect is consistent across studies and combining results across studies yields increased power. Most meta-analysis approaches essentially compute a weighted average from the results of the individual studies, and larger studies tend to be given more weight.

Randomization or random assignment

In randomized trials, the participants are assigned by chance to the treatment groups (arms) rather than by choice. Randomization serves to make the groups similar with respect to variables (e.g., patient characteristics, tumor traits) other than the treatment. This means if differences are observed for the outcome variable (primary endpoint), it can be attributable to the treatment since the groups balanced for the other variables. Randomization is accomplished with a chance procedure (e.g., flipping a coin) or a random number generator.

Stratification variable

A stratification variable in a clinical trial is a variable that is used to group patients into strata corresponding to the values of the variable. Randomization is performed separately within each stratum. An example of a stratification variable is whether a patient has disease in his/her lymph nodes or not (e.g., lymph node status with values of lymph node positive and lymph node negative). Variables selected for stratification are those where it is important there is no imbalance between the treatment arms because they are highly prognostic of outcome.

Type I error

Type I error is the error that occurs when the null hypothesis is rejected although it is true. It is a false-positive result. For example, suppose in reality there is no difference between the experimental treatment and standard of care with respect to overall survival. However, a clinical trial is performed, and it is found that the treatment arm had superior survival compared to the standard of care arm with a p-value of 0.03. The investigators conclude that the experimental treatment is better than the standard of care. In reality, this is an incorrect conclusion and an example of a type I error. (Note that the investigators would not know that their conclusion is incorrect.)

Univariable hazards ratio (HR)

A univariable hazard ratio is the ratio of hazard rates for an event (e.g., death) corresponding to the different values of one variable of interest. For example, in a Cox model that contains only a treatment variable (experimental versus control), a HR = 0.50 for survival indicates that patients in the treatment group die at half the rate per unit of time as patients in the control group.

References

  1. 1.
    Tsao MS, Le Teuff G, Shepherd FA, Landais C, Hainaut P, Filipits M, Pirker R, Le Chevalier T, Graziano S, Kratze R, Soria JC, Pignon JP, Seymour L, Brambilla E. PD-L1 protein expression assessed by immunohistochemistry is neither prognostic nor predictive of benefit from adjuvant chemotherapy in resected non-small cell lung cancer. Ann Oncol. 2017;28(4):882–9.  https://doi.org/10.1093/annonc/mdx003. PubMed PMID: 28137741.CrossRefPubMedPubMedCentralGoogle Scholar
  2. 2.
    Baselga J, Cortés J, Im SA, Clark E, Ross G, Kiermaier A, Swain SM. Biomarker analyses in CLEOPATRA: a phase III, placebo-controlled study of pertuzumab in human epidermal growth factor receptor 2-positive, first-line metastatic breast cancer. J Clin Oncol. 2014;32(33):3753–61.  https://doi.org/10.1200/JCO.2013.54.5384. Epub 2014 Oct 20. PubMed PMID: 25332247.CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Van Cutsem E, Lenz HJ, Köhne CH, Heinemann V, Tejpar S, Melezínek I, Beier F, Stroh C, Rougier P, van Krieken JH, Ciardiello F. Fluorouracil, leucovorin, and irinotecan plus cetuximab treatment and RAS mutations in colorectal cancer. J Clin Oncol. 2015;33(7):692–700.  https://doi.org/10.1200/JCO.2014.59.4812. Epub 2015 Jan 20. PubMed PMID: 25605843.CrossRefGoogle Scholar
  4. 4.
    Brugger W, Triller N, Blasinska-Morawiec M, Curescu S, Sakalauskas R, Manikhas GM, Mazieres J, Whittom R, Ward C, Mayne K, Trunzer K, Cappuzzo F. Prospective molecular marker analyses of EGFR and KRAS from a randomized, placebo-controlled study of erlotinib maintenance therapy in advanced non-small-cell lung cancer. J Clin Oncol. 2011;29(31):4113–20.  https://doi.org/10.1200/JCO.2010.31.8162. Epub 2011 Oct 3. Erratum in: J Clin Oncol. 2011 Dec 10;29(35):4725. PubMed PMID: 21969500.CrossRefGoogle Scholar
  5. 5.
    Rowland A, Dias MM, Wiese MD, Kichenadasse G, McKinnon RA, Karapetis CS, Sorich MJ. Meta-analysis of BRAF mutation as a predictive biomarker of benefit from anti-EGFR monoclonal antibody therapy for RAS wild-type metastatic colorectal cancer. Br J Cancer. 2015;112(12):1888–94.  https://doi.org/10.1038/bjc.2015.173. Epub 2015 May 19. Review. PubMed PMID: 25989278; PubMed Central PMCID: PMC4580381.CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Romond EH, Perez EA, Bryant J, Suman VJ, Geyer CE Jr, Davidson NE, Tan-Chiu E, Martino S, Paik S, Kaufman PA, Swain SM, Pisansky TM, Fehrenbacher L, Kutteh LA, Vogel VG, Visscher DW, Yothers G, Jenkins RB, Brown AM, Dakhil SR, Mamounas EP, Lingle WL, Klein PM, Ingle JN, Wolmark N. Trastuzumab plus adjuvant chemotherapy for operable HER2-positive breast cancer. N Engl J Med. 2005;353(16):1673–84. PubMed PMID: 16236738.CrossRefGoogle Scholar
  7. 7.
    Rugo HS, Olopade OI, DeMichele A, Yau C, van ’t Veer LJ, Buxton MB, Hogarth M, Hylton NM, Paoloni M, Perlmutter J, Symmans WF, Yee D, Chien AJ, Wallace AM, Kaplan HG, Boughey JC, Haddad TC, Albain KS, Liu MC, Isaacs C, Khan QJ, Lang JE, Viscusi RK, Pusztai L, Moulder SL, Chui SY, Kemmer KA, Elias AD, Edmiston KK, Euhus DM, Haley BB, Nanda R, Northfelt DW, Tripathy D, Wood WC, Ewing C, Schwab R, Lyandres J, Davis SE, Hirst GL, Sanil A, Berry DA, Esserman LJ, I-SPY 2 Investigators. Adaptive randomization of veliparib-carboplatin treatment in breast cancer. N Engl J Med. 2016;375(1):23–34.  https://doi.org/10.1056/NEJMoa1513749.CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Park JW, Liu MC, Yee D, Yau C, van ’t Veer LJ, Symmans WF, Paoloni M, Perlmutter J, Hylton NM, Hogarth M, DeMichele A, Buxton MB, Chien AJ, Wallace AM, Boughey JC, Haddad TC, Chui SY, Kemmer KA, Kaplan HG, Isaacs C, Nanda R, Tripathy D, Albain KS, Edmiston KK, Elias AD, Northfelt DW, Pusztai L, Moulder SL, Lang JE, Viscusi RK, Euhus DM, Haley BB, Khan QJ, Wood WC, Melisko M, Schwab R, Helsten T, Lyandres J, Davis SE, Hirst GL, Sanil A, Esserman LJ, Berry DA, I-SPY 2 Investigators. Adaptive randomization of Neratinib in early breast cancer. N Engl J Med. 2016;375(1):11–22.  https://doi.org/10.1056/NEJMoa1513750.CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Kim ES, Herbst RS, Wistuba II, Jack Lee J, Blumenschein GR Jr, Tsao A, Stewart DJ, Hicks ME, Erasmus J Jr, Gupta S, Alden CM, Liu S, Tang X, Khuri FR, Tran HT, Johnson BE, Heymach JV, Li M, Fossella F, Kies MS, Papadimitrakopoulou V, Davis SE, Lippman SM, Hong WK. The BATTLE trial: personalizing therapy for lung cancer. Cancer Discov. 2011;  https://doi.org/10.1158/2159-8274.CD-10-0010.CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Papadimitrakopoulou V, Jack Lee J, Wistuba II, Tsao AS, Fossella FV, Kalhor N, Gupta S, Byers LA, Izzo JG, Gettinger SN, Goldberg SB, Tang X, Miller VA, Skoulidis F, Gibbons DL, Li S, Wei C, Diao L, Andrew Peng S, Wang J, Tam AL, Coombes KR, Ja SK, Mauro DJ, Rubin EH, Heymach JV, Hong WK, Herbst RS. The BATTLE-2 study: a biomarker-integrated targeted therapy study in previously treated patients with advanced non–small-cell lung cancer. JCO. 2016;34(30):3638–47.CrossRefGoogle Scholar
  11. 11.
    Steuer CE1, Papadimitrakopoulou V, Herbst RS, Redman MW, Hirsch FR, Mack PC, Ramalingam SS, Gandara DR. Innovative clinical trials: the LUNG-MAP study. Clin Pharmacol Ther. 2015;97(5):488–91.  https://doi.org/10.1002/cpt.88.CrossRefPubMedGoogle Scholar
  12. 12.
    Lih CJ, Sims DJ, Harrington RD, Polley EC, Zhao Y, Mehaffey MG, Forbes TD, Das B, Walsh WD, Datta V, Harper KN, Bouk CH, Rubinstein LV, Simon RM, Conley BA, Chen AP, Kummar S, Doroshow JH, Williams PM. Analytical validation and application of a targeted next-generation sequencing mutation-detection assay for use in treatment assignment in the NCI-MPACT trial. J Mol Diagn. 2016;18(1):51–67.  https://doi.org/10.1016/j.jmoldx.2015.07.006.CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Moore KN, Mannel RS. Is the NCI MATCH trial a match for gynecologic oncology? Gynecol Oncol. 2016;140(1):161–6.  https://doi.org/10.1016/j.ygyno.2015.11.003. Review.CrossRefPubMedGoogle Scholar
  14. 14.
    Cappuzzo F, Ciuleanu T, Stelmakh L, Cicenas S, Szczésna A, Juhász E, Esteban E, Molinier O, Brugger W, Melezínek I, Klingelschmitt G, Klughammer B, Giaccone G. SATURN investigators. Erlotinib as maintenance treatment in advanced non-small-cell lung cancer: a multicentre, randomised, placebo-controlled phase 3 study. Lancet Oncol. 2010;11(6):521–9.  https://doi.org/10.1016/S1470-2045(10)70112-1. Epub 2010 May 20. PubMed PMID: 20493771.CrossRefPubMedGoogle Scholar
  15. 15.
    Hyman DM, Pazanov I, Subbiah V, Faris JE, Chau I, Blay JY, Wolf J, Raje NS, Diamond EL, Hollebecque A, Gervais R, Elez-Fernandez ME, Italiano A, Hofheinz RD, Hidalgo M, Chan E, Schuler M, Lasserre SF, Makrutzki M, Sirzen F, Veronese ML, Tabernero J, Baselga J. Vemurafenib in multiple nonmelanoma cancers with BRAF V600 mutations. N Engl J Med. 2015;373:726–36.  https://doi.org/10.1056/NEJMoa150230.CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    Kim ES, Herbst RS, Wistuba II, Lee JJ, Blumenschein GR Jr, Tsao A, Stewart DJ, Hicks ME, Erasmus J Jr, Gupta S, Alden CM, Liu S, Tang X, Khuri FR, Tran HT, Johnson BE, Heymach JV, Mao L, Fossella F, Kies MS, Papadimitrakopoulou V, Davis SE, Lippman SM, Hong WK. The BATTLE trial: personalizing therapy for lung cancer. Cancer Discov. 2011;1(1):44–53.  https://doi.org/10.1158/2159-8274.CD-10-0010. Epub 2011 Jun 1. PubMed PMID: 22586319; PubMed Central PMCID: PMC4211116.CrossRefPubMedPubMedCentralGoogle Scholar
  17. 17.
    Barker AD, Sigman CC, Kelloff GJ, Hylton NM, Berry DA, Esserman LJ. I-SPY 2: an adaptive breast cancer trial design in the setting of neoadjuvant chemotherapy. Clin Pharmacol Ther. 2009;86(1):97–100.  https://doi.org/10.1038/clpt.2009.68. Epub 2009 May 13. PubMed PMID: 19440188.CrossRefPubMedGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Healthcare Policy and Research, Division of Biostatistics and EpidemiologyWeill Cornell MedicineNew YorkUSA

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