Impact of time-varying cumulative bevacizumab exposures on survival: re-analysis of data from randomized clinical trial in patients with metastatic colo-rectal cancer

Adrien Guilloteau, Michal Abrahamowicz, Olayide Boussari, Valérie Jooste, Thomas Aparicio, Catherine Quantin, Karine Le Malicot, Christine Binquet, Adrien Guilloteau, Michal Abrahamowicz, Olayide Boussari, Valérie Jooste, Thomas Aparicio, Catherine Quantin, Karine Le Malicot, Christine Binquet

Abstract

Background: As cancer treatment, biotherapies can be as effective as chemotherapy while reducing the risk of secondary effects, so that they can be taken over longer periods than conventional chemotherapy. Thus, some trials aimed at assessing the benefit of maintaining biotherapies during chemotherapy-free intervals (CFI). For example, the recent PRODIGE9 trial assessed the effect of maintaining bevacizumab during CFI in metastatic colorectal cancer (mCRC) patients. However, its analysis was hindered by a small difference of exposure to the treatment between the randomized groups and by a large proportion of early drop outs, leading to a potentially unbalanced distribution of confounding factors among the trial completers. To address these limitations, we re-analyzed the PRODIGE9 data to assess the effects of different exposure metrics on all-cause mortality of patients with mCRC using methods originally developed for observational studies.

Methods: To account for the actual patterns of drug use by individual patients and for possible cumulative effects, we used five alternative time-varying exposure metrics: (i) cumulative dose, (ii) quantiles of the cumulative dose, (iii) standardized cumulative dose, (iv) Theoretical Blood Concentration (TBC), and (v) Weighted Cumulative Exposure (WCE). The last two metrics account for the timing of drug use. Treatment effects were estimated using adjusted Hazard Ratio from multivariable Cox proportional hazards models.

Results: After excluding 112 patients who died during the induction period, we analyzed data on 382 patients, among whom 320 (83.8%) died. All time-varying exposures improved substantially the model's fit to data, relative to using only the time-invariant randomization group. All exposures indicated a protective effect for higher cumulative bevacizumab doses. The best-fitting WCE and TBC models accounted for both the cumulative effects and the different impact of doses taken at different times.

Conclusions: All time-varying analyses, regardless of the exposure metric used, consistently suggested protective effects of higher cumulative bevacizumab doses. However, the results may partly reflect the presence of a confusion bias. Complementing the main ITT analysis of maintenance trials with an analysis of potential cumulative effects of treatment actually taken can provide new insights, but the results must be interpreted with caution because they do not benefit from the randomization.

Trial registration: clinicaltrials.gov, NCT00952029 . Registered 8 August 2009.

Keywords: Bevacizumab; Colorectal cancer; Survival; Time varying cumulative exposure to maintenance treatment.

Conflict of interest statement

TA has been paid for consultant work by BMS and Halio DX and has been reimbursed by Servier, Amgen, Ipsen, BMS, Roche and Bayer for international conference attendance or presentation. Other authors declare that there is no conflict of interest.

Figures

Fig. 1
Fig. 1
Cumulative exposure to bevacizumab at 60, 120, 182, 365 and 730 days after baseline. One unit on the x-axis is equivalent to the dose of bevacizumab received during one cure (5 mg/kg)
Fig. 2
Fig. 2
Hazard-ratios of the Weighted Cumulative Exposure (WCE) model. Changes in HRs of death with increasing time from the day of dose intake, for patients who were administered 1, 2 or 10 dose with patients who received no doses as the reference (gray shaded area indicates the 95% pointwise confidence band interval for the HR for the “1 dose” exposure)

References

    1. Hegewisch-Becker S, Graeven U, Lerchenmüller CA, Killing B, Depenbusch R, Steffens C-C, et al. Maintenance strategies after first-line oxaliplatin plus fluoropyrimidine plus bevacizumab for patients with metastatic colorectal cancer (AIO 0207): a randomised, non-inferiority, open-label, phase 3 trial. Lancet Oncol. 2015;16(13):1355–1369.
    1. Sunakawa Y, Bekaii-Saab T, Stintzing S. Reconsidering the benefit of intermittent versus continuous treatment in the maintenance treatment setting of metastatic colorectal cancer. Cancer Treat Rev. 2016;45:97–104.
    1. Pavlidis ET, Pavlidis TE. Role of bevacizumab in colorectal cancer growth and its adverse effects: a review. World J Gastroenterol WJG. 2013;19(31):5051–5060.
    1. Tamburini E, Rudnas B, Santelmo C, Drudi F, Gianni L, Nicoletti SVL, et al. Maintenance based Bevacizumab versus complete stop or continuous therapy after induction therapy in first line treatment of stage IV colorectal cancer: a meta-analysis of randomized clinical trials. Crit Rev Oncol Hematol. 2016;104:115–123.
    1. Xu W, Gong Y, Kuang M, Wu P, Cao C, Chen J, et al. Survival benefit and safety of Bevacizumab in combination with Erlotinib as maintenance therapy in patients with metastatic colorectal Cancer: a meta-analysis. Clin Drug Investig. 2017;37(2):155–165.
    1. Aparicio T, Linot B, Le Malicot K, Bouché O, Boige V, François E, et al. FOLFIRI+bevacizumab induction chemotherapy followed by bevacizumab or observation in metastatic colorectal cancer, a phase III trial (PRODIGE 9--FFCD 0802) Dig Liver Dis Off J Ital Soc Gastroenterol Ital Assoc Study Liver. 2015;47(4):271–272.
    1. Mansournia MA, Higgins JPT, Sterne JAC, Hernán MA. Biases in randomized trials: a conversation between trialists and epidemiologists. Epidemiol Camb Mass. 2017;28(1):54–59.
    1. Carlomagno C, De Stefano A, Rosanova M, De Falco S, Attademo L, Fiore G, et al. Multiple treatment lines and prognosis in metastatic colorectal cancer patients. Cancer Metastasis Rev. 2019;38(1–2):307–13.
    1. Greenland S, Robins JM, Pearl J. Confounding and collapsibility in causal inference. Stat Sci. 1999;14(1):29–46.
    1. Montedori A, Bonacini MI, Casazza G, Luchetta ML, Duca P, Cozzolino F, et al. Modified versus standard intention-to-treat reporting: are there differences in methodological quality, sponsorship, and findings in randomized trials? A cross-sectional study. Trials. 2011;12:58.
    1. Pocock SJ, McMurray JJV, Collier TJ. Statistical controversies in reporting of clinical trials: part 2 of a 4-part series on statistics for clinical trials. J Am Coll Cardiol. 2015;66(23):2648–2662.
    1. Cox DR. Regression models and life-tables. J R Stat Soc Ser B Methodol. 1972;34(2):187–220.
    1. Wozniak KM, Vornov JJ, Wu Y, Nomoto K, Littlefield BA, DesJardins C, et al. Sustained accumulation of microtubule-binding chemotherapy drugs in the peripheral nervous system: correlations with time course and neurotoxic severity. Cancer Res. 2016;76(11):3332–3339.
    1. Mohelnikova-Duchonova B, Melichar B, Soucek P. FOLFOX/FOLFIRI pharmacogenetics: the call for a personalized approach in colorectal cancer therapy. World J Gastroenterol WJG. 2014;20(30):10316–30.
    1. Farran B, McGurnaghan S, Looker HC, Livingstone S, Lahnsteiner E, Colhoun HM, et al. Modelling cumulative exposure for inference about drug effects in observational studies. Pharmacoepidemiol Drug Saf. 2017;26(12):1527–1533.
    1. Abrahamowicz M, Beauchamp M-E, Sylvestre M-P. Comparison of alternative models for linking drug exposure with adverse effects. Stat Med. 2012;31(11–12):1014–1030.
    1. de Vocht F, Burstyn I, Sanguanchaiyakrit N. Rethinking cumulative exposure in epidemiology, again. J Expo Sci Environ Epidemiol. 2015;25(5):467–473.
    1. Wang M, Liao X, Laden F, Spiegelman D. Quantifying risk over the life course - latency, age-related susceptibility, and other time-varying exposure metrics. Stat Med. 2016;35(13):2283–2295.
    1. Robinson DE, van Staa TP, Dennison EM, Cooper C, Dixon WG. The limitations of using simple definitions of glucocorticoid exposure to predict fracture risk: a cohort study. Bone. 2018;117:83–90.
    1. Alsabbagh MW, Eurich D, Lix LM, Wilson TW, Blackburn DF. Does the association between adherence to statin medications and mortality depend on measurement approach? A retrospective cohort study. BMC Med Res Methodol. 2017;17(1):66.
    1. Abrahamowicz M, Bartlett G, Tamblyn R, du Berger R. Modeling cumulative dose and exposure duration provided insights regarding the associations between benzodiazepines and injuries. J Clin Epidemiol. 2006;59(4):393–403.
    1. Sylvestre M-P, Abrahamowicz M. Flexible modeling of the cumulative effects of time-dependent exposures on the hazard. Stat Med. 2009;28(27):3437–3453.
    1. Gasparrini A. Modeling exposure-lag-response associations with distributed lag non-linear models. Stat Med. 2014;33(5):881–899.
    1. Aparicio T, Ghiringhelli F, Boige V, Le Malicot K, Taieb J, Bouché O, et al. Bevacizumab maintenance versus no maintenance during chemotherapy-free intervals in metastatic colorectal Cancer: a randomized phase III trial (PRODIGE 9) J Clin Oncol Off J Am Soc Clin Oncol. 2018;36(7):674–681.
    1. Eisenhauer EA, Therasse P, Bogaerts J, Schwartz LH, Sargent D, Ford R, et al. New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1) Eur J Cancer Oxf Engl 1990. 2009;45(2):228–247.
    1. Wainwright NWJ, Surtees PG. Time-varying exposure and the impact of stressful life events on onset of affective disorder. Stat Med. 2002;21(14):2077–2091.
    1. Panoilia E, Schindler E, Samantas E, Aravantinos G, Kalofonos HP, Christodoulou C, et al. A pharmacokinetic binding model for bevacizumab and VEGF165 in colorectal cancer patients. Cancer Chemother Pharmacol. 2015;75(4):791–803.
    1. Xiao Y, Abrahamowicz M, Moodie EEM, Weber R, Young J. Flexible marginal structural models for estimating the cumulative effect of a time-dependent treatment on the Hazard: reassessing the cardiovascular risks of Didanosine treatment in the Swiss HIV cohort study. J Am Stat Assoc. 2014;109(506):455–464.
    1. Danieli C, Abrahamowicz M. Competing risks modeling of cumulative effects of time-varying drug exposures. Stat Methods Med Res. 2019;28(1):248–262.
    1. Sylvestre M-P, Beauchamp M-E, Kyle RP, Abrahamowicz M. WCE: Weighted Cumulative Exposure Models. R Package [Internet]. Available from: .
    1. Quantin C, Abrahamowicz M, Moreau T, Bartlett G, MacKenzie T, Tazi MA, et al. Variation over time of the effects of prognostic factors in a population-based study of colon cancer: comparison of statistical models. Am J Epidemiol. 1999;150(11):1188–1200.
    1. Hernan MA, Brumback B, Robins JM. Marginal structural models to estimate the joint causal effect of nonrandomized treatments. J Am Stat Assoc. 2001;96(454):440–448.
    1. Rui Y, Wang C, Zhou Z, Zhong X, Yu Y. K-Ras mutation and prognosis of colorectal cancer: a meta-analysis. Hepatogastroenterology. 2015;62(137):19–24.
    1. Jang HJ, Kim BJ, Kim JH, Kim HS. The addition of bevacizumab in the first-line treatment for metastatic colorectal cancer: an updated meta-analysis of randomized trials. Oncotarget. 2017;8(42):73009–73016.

Source: PubMed

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