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Center for Global Research Data

Predictors of exposure, therapeutic and adverse effects of atezolizumab used in the treatment of advanced cancers

Lead Investigator: Ashley Hopkins, Flinders University
Title of Proposal Research: Predictors of exposure, therapeutic and adverse effects of atezolizumab used in the treatment of advanced cancers
Vivli Data Request: 6117, 5895
Funding Source: None
Potential Conflicts of Interest: None

Summary of the Proposed Research:

Advanced cancers are difficult to treat. Atezolizumab is an immunotherapy which are an important emerging treatment option for advanced cancer patients. However, response and toxicity to immunotherapies is highly unpredictable, with up to 40% of the patients who initiate atezolizumab not responding, and 40% experiencing serious toxicity. Using the diverse range of data collected from clinical trials, it is possible to develop clinical tools that enable improved prediction of therapeutic and adverse outcomes of patients using atezolizumab in the treatment of advanced cancers. Being able to identify the expected response and adverse effect profile may enable patients and clinicians to make better decisions regarding whether to commence, continue, discontinue or change dosing of atezolizumab.

Statistical Analysis plan:

General model development:

Cox-proportional hazard / time-to-event models will be used to assess the association between potential predictors and the time to an adverse effect or survival time. Associations will be reported primarily as hazard ratios with 95% confidence intervals. The association of potential predictors with binary outcomes (e.g. best overall response) will be modelled using logistic regression and will be reported as odds ratios with 95% confidence intervals. Longitudinal analysis (e.g., linear and non-linear mixed effect modelling) will be used to assess the nature and patterns of longitudinal changes of key continuous variables (e.g. drug concentration, tumour size, neutrophil counts).

Software:

The R Software (R Core Team) will be used for data preparation, modelling and graphical output.

Covariate analyses:

Potential predictors will be prioritised according to biological/clinical plausibility and prior evidence of association with the relevant outcome (adverse events, therapeutic response, drug exposure). Should multiple values of a covariate be recorded multiple times for a single visit (e.g. blood pressure) the mean of the multiple reads taken at each visit will be used. Crude associations will be reported based on univariate analysis (adjusting only for the clinical trial and where appropriate the cancer medicine), and adjusted associations based on a multivariable analysis. Continuous variables will be assessed for non-linearity of association with the outcome using restricted cubic splines. Clinical prediction models will be developed using multivariable analysis and will generally include all available known baseline predictors of the outcome of interest as well as covariates identified in univariate analysis. Penalised models will be used to minimize risk of overfitting. Early markers of exposure, response and toxicity will be primarily evaluated using a landmark approach where possible, with sensitivity analyses based on the use of time-dependent covariates. Landmark time will be dependent on the time points available in individual studies, and the time frame of changes in each specific predictor variable. As this analysis is primarily hypothesis generating and will require subsequent validation of any findings, no formal adjustment for multiple testing is intended. However, this limitation will be clearly stated in any publications of results. As it is expected that < 5% of data will be missing for any variable a complete case analysis is planned. Should variables with substantial missing data be present, the pattern and likely cause of the missing data will be evaluated and if missing at random is reasonable to assume then single regression imputation will be undertaken.
Analyses will also include evaluating the heterogeneity in toxicity incidence and response profiles according to model risk for atezolizumab as compared to relevant comparator arms, as well as evaluating the predictors of the main adverse effects and response profiles for the medicines used in the comparator arms. Such analyses will allow a better understanding of the benefits of specific therapies, and whether the relationships identified are specific to atezolizumab or are common.

Power:

Predictors that have a clinically meaningful (e.g., double the risk) effect on mortality and adverse effects will be of primary interest. Based upon a 30% incidence of toxicity, a sample size of approximately 600 is required to detect a predictor (with a 10% frequency within the population) associated with a two-fold risk (α=0.05 with 80% power). Based upon an event rate of 40% during trial follow-up (e.g., for progression), approximately 450 participants are required for 80% power to detect a predictor (with a 10% frequency within the population) associated with a two-fold hazard of the event (α=0.05). Reference: Chow S, Shao J, Wang H. 2008. Sample Size Calculations in Clinical Research. 2nd Ed. Chapman & Hall/CRC Biostatistics Series. page 177. These samples sizes are well within scope for this study. Sample sizes greater than this will allow the investigation of more complex relationships with greater predictive performance, which is a primary objective of this study.

Quality Control:

Data will be explored for inconsistencies in time recordings, physiologically unreasonable covariate values, and unit errors. Prior to beginning analyses, individual data values will be extracted/constructed based on the raw and analysis datasets provided. To ensure that each variable has been correctly extracted/constructed from the data provided, basic analyses and descriptive statistics will be reproduced to check for consistency with pertinent results in published manuscripts or CSRs relating to the specific trial. Where there are insufficient published results to confirm the proper extraction of the variable, the extracted values will be manually checked against a random sample of the original dataset values.

Requested Studies:

A Phase III, Open-Label, Multicenter, Randomized Study to Investigate the Efficacy and Safety of Atezolizumab (Anti-PD-L1 Antibody) Compared With Docetaxel in Patients With Non-Small Cell Lung Cancer After Failure With Platinum Containing Chemotherapy
Sponsor: Roche
Study ID: NCT02008227
Sponsor ID: GO28915

A Phase II, Open-label, Multicenter, Randomized Study to Investigate the Efficacy and Safety of MPDL3280A (Anti−PD-L1 Antibody) Compared With Docetaxel in Patients With Non−Small Cell Lung Cancer After Platinum Failure
Sponsor: Roche
Study ID: NCT01903993
Sponsor ID: GO28753

A Phase II, Multicenter, Single-Arm Study OF Atezolizumab In Patients With PD-L1-Positive Locally Advanced Or Metastatic Non-Small Cell Lung Cancer
Sponsor: Roche
Study ID: NCT02031458
Sponsor ID: GO28754

A Phase II, Multicenter, Single-arm Study of MPDL3280A in Patients With PD-L1-Positive Locally Advanced or Metastatic Non-small Cell Lung Cancer
Sponsor: Roche
Study ID: NCT01846416
Sponsor ID: GO28625

A Phase II, Multicenter, Single-Arm Study of Atezolizumab in Patients With Locally Advanced or Metastatic Urothelial Bladder Cancer
Sponsor: Roche
Study ID: NCT02951767
Sponsor ID: GO29293 (Cohort 1)

A Phase II, Multicenter, Single-Arm Study of Atezolizumab in Patients With Locally Advanced or Metastatic Urothelial Bladder Cancer
Sponsor: Roche
Study ID: NCT02108652
Sponsor ID: GO29293 (Cohort 2)

A Phase III, Open-Label, Multicenter, Randomized Study to Investigate the Efficacy and Safety of Atezolizumab (Anti-PD-L1 Antibody) Compared With Chemotherapy in Patients With Locally Advanced or Metastatic Urothelial Bladder Cancer After Failure With Platinum-Containing Chemotherapy
Sponsor: Roche
Study ID: NCT02302807
Sponsor ID: GO29294

A Phase II, Randomized Study of Atezolizumab (Anti-PD-L1 Antibody) Administered as Monotherapy or in Combination With Bevacizumab Versus Sunitinib in Patients With Untreated Advanced Renal Cell Carcinoma
Sponsor: Roche
Study ID: NCT01984242
Sponsor ID: WO29074

(Note: Additional studies added as part of Data Request 6117)

A Phase III, Open-Label, Randomized Study of Atezolizumab (Anti-PD-L1 Antibody) in Combination With Bevacizumab Versus Sunitinib in Patients With Untreated Advanced Renal Cell Carcinoma
Sponsor: Roche
Study ID: NCT02420821
Sponsor ID: WO29637

A Phase III, Open-Label, Multicenter, Randomized Study Evaluating the Efficacy and Safety of Atezolizumab (MPDL3280A, Anti-PD-L1 Antibody) in Combination With Carboplatin+Paclitaxel or Atezolizumab in Combination With Carboplatin+Nab-Paclitaxel Versus Carboplatin+Nab-Paclitaxel in Chemotherapy-Naive Patients With Stage IV Squamous Non-Small Cell Lung Cancer
Sponsor: Roche
Study ID: NCT02367794
Sponsor ID: GO29437

A Phase III Multicenter, Randomized, Open-Label Study Evaluating the Efficacy and Safety of Atezolizumab (MPDL3280A, Anti-PD-L1 Antibody) in Combination With Carboplatin+Nab-Paclitaxel for Chemotherapy-Naive Patients With Stage IV Non-Squamous Non-Small Cell Lung Cancer
Sponsor: Roche
Study ID: NCT02367781
Sponsor ID: GO29537

A Multistage, Phase II Study Evaluating the Safety and Efficacy of Cobimetinib Plus Paclitaxel, Cobimetinib Plus Atezolizumab Plus Paclitaxel, or Cobimetinib Plus Atezolizumab Plus Nab-Paclitaxel as First-Line Treatment for Patients With Metastatic Triple-Negative Breast Cancer
Sponsor: Roche
Study ID: NCT02322814
Sponsor ID: WO29479

Publication:

Hopkins AM, Kichenadasse G, Abuhelwa AY, McKinnon RA, Rowland A, Sorich MJ. Value of the Lung Immune Prognostic Index in Patients with Non-Small Cell Lung Cancer Initiating First-Line Atezolizumab Combination Therapy: Subgroup Analysis of the IMPOWER150 Trial. Cancers. 2021; 13(5):1176. DOI: 10.3390/cancers13051176

Abuhelwa AY, Kichenadasse G, McKinnon RA, Rowland A, Hopkins AM, Sorich MJ. Machine Learning for Prediction of Survival Outcomes with Immune-Checkpoint Inhibitors in Urothelial Cancer. Cancers. 2021; 13(9):2001. DOI: 10.3390/cancers13092001

Concomitant Proton Pump Inhibitor Use and Survival in Urothelial Carcinoma Treated with Atezolizumab. Ashley M. Hopkins, Ganessan Kichenadasse, Christos S. Karapetis, Andrew Rowland and Michael J. Sorich.
Clin Cancer Res October 15 2020 (26) (20) 5487-5493; DOI: 10.1158/1078-0432.CCR-20-1876

Hopkins, A.M., Kichenadasse, G., McKinnon, R.A. et al. Efficacy of first-line atezolizumab combination therapy in patients with non-small cell lung cancer receiving proton pump inhibitors: post hoc analysis of IMpower150. Br J Cancer (2021). doi.org/10.1038/s41416-021-01606-4

Hopkins, A. M., Abuhelwa, A. Y., McKinnon, R. A., Logan, J. M., Kichenadasse, G., Rowland, A., & Sorich, M. J. (2021). Smoking and immunotherapy efficacy in lung cancer by PDL1 subgroups: An individual participant data meta-analysis of atezolizumab clinical trials. European Journal of Cancer. Doi:10.1016/j.ejca.2021.10.020