Background:

1. Multi trial evaluation of longitudinal tumor measurement (TM)-based metrics for predicting overall survival (OS) using the RECIST 1.1 data warehouse Background Definitions of Metrics • Average Baseline Sum (in mm) • Sum of one-dimensional baseline tumor measurements of consistent lesions / number of consistent lesions • Tumor measurements are recorded in millimeters (mm). • First slope (0-6 wks) and last slope (6-12 wks) • (m6 – m0) / 6 and (m12 – m6) / 6; mx = tumor measurement at week x • Units: millimeters per week (mm/w) • Indicator of (first slope > 0) = 1, if first slope >0; 0 otherwise • Similarly for last slope-based metrics • First % change (0-6 wks) and last % change (6-12 wks) • 10*(m6 – m0) /(6*m0) and 10* (m12 – m6) /(6*m6) • mx = tumor measurement at week x • Units: 10% change per week (10%/w) • Indicator of (first % change > 0) = 1, if first % change>0; 0 otherwise • Similarly for last % change-based metrics • Indicator of (inflection status) = 1, if inflection; 0 otherwise • Background: • Response Evaluation Criteria in Solid Tumors (RECIST) version 1.0 (and RECIST version1.1) for measuring tumor shrinkage groups patients into categories based on change in tumor measurements, specifically: • Complete response - Complete disappearance of all lesions • Partial response - at least 30% reduction from baseline sum for target lesions • Progression - at least 20% increase from the lowest sum of measurements (and at least 5 mm absolute increase in version 1.1) or new lesion recorded (with additional FDG PET assessment in version 1.1) • Stable otherwise • We previously reported (ASCO 2012) that alternative cutpoints and alternate categorical metrics to RECIST standards provided no meaningful improvement in overall survival (OS) prediction Therasse et al., JNCI 2000; Eisenhauer et al., EJC 2009 Methods Summary • Regardless of tumor type: • TM based metrics had similar predictive performance compared to RECIST based categorical metrics. • Although point estimates were higher, the 95% CIs for the TM based models encompass the RECIST c-index • Smaller sample size for some of the training and validation cohorts • Theoretical c-index much higher than TM based and RECIST based metrics Sumithra J. Mandrekar, Ph.D.1, Ming-Wen An, Ph.D.2, Xinxin Dong, Ph.D.3, Axel Grothey, M.D.1, Jan Bogaerts, M.D.4, Daniel J. Sargent, Ph.D.1, 1Mayo Clinic, Rochester, MN, USA; 2Vassar College, Poughkeepsie, NY, USA; 3University of Pittsburgh, Pittsburgh, PA, USA; 4European Organization for Research and Treatment of Cancer Headquarters, Brussels, Belgium Results Results Imaging • c-indices (and 95% CI) from the training set for the slope-based and % change-based models Imaging assessment schedule per study Slope-based model % change-based model Point estimates for the C-indices *: slope-based model, % change-based model Research Question • Are there continuous, longitudinal TM-based metrics that can enhance prediction of OS outcomes compared to RECIST based categorical metrics? • Goals: • Identify and validate clinically relevant (necessary & sufficient) features of the tumor trajectory for overall survival (OS) prediction. • Compare the longitudinal TM-based metrics to RECIST based categorical metrics for OS prediction • Landmark analysis at 12 weeks • Window around landmark time point: keeping only those who are alive beyond the landmark time point, with available tumor status at landmark time point +/- 2 weeks • Outcome: OS (time from registration to death from any cause) • Cox PH models, stratified by study and number of consistent lesions (< 3 and >= 3), and adjusted for average baseline tumor sum • Separate models for each tumor type • Excluded the following due to lack of (reliable)TM measurements: • Progression due to new lesions • Assessments based on clinical examination only • 60:40 split (training: test), stratified by: • survival status, progression status, and “perfect status” (if observed assessments are within 2 weeks of protocol expected assessments based on a sliding window) • Selection criterion: concordance index (with associated 95% CI) • Theoretical upper bound for c-index: calculated from time-dependent Cox models using PFS as time-dependent status, using all available data • Breast: 0.66 • Lung: 0.67 • Colon: 0.68 Models • An Example: • Consider 2 patients with the same average baseline slope and last slope, but with different first slopes (using the colon cancer training set): • That is, the HR associated with a 1mm/w increase in first slope depends on whether the first slopes are positive or negative. • An Example: • Consider 2 patients with the same average baseline slope and last % change, but with different first % changes (using the colon cancer training set): • That is, the HR associated with a 10%/w increase in first % change depends on whether the first % changes are positive or negative. • Slope based: • Similar interpretation for the last slope metrics. • The % change based metrics (for 10% change) follows a similar model Sample Data Limitations • Trajectories of 10 randomly selected patients from each study • Analysis based on data from only 3 tumor types • Missing data issues: • Not all lesions measured over time • Missed visits, or missing assessments due to only clinical evaluations • Primarily in breast cancer leading to small sample size for the models investigated • Missing measurements on target lesions when progression was from new or non-target lesions • Censored predictor variables • No TM measurements after RECIST progression • Landmark analysis: Conditional on survival to 12 weeks • Alternative: Include a time-dependent component prior to 12 weeks to account for early deaths, progressions, drop outs etc. Acknowledgements  2013 Mayo Foundation for Medical Education and Research • RECIST steering committee • Supported in part by National Institute grant, CA167326-01