Obinutuzumab is a second-generation anti-CD20 antibody targeted to improve outcome in three major lymphoma indications. In 2011, Roche launched a full development program for Obinutuzumab with four randomized Phase 3 trials, all with progression-free survival (PFS) as primary endpoint. Each of these trials compared Obinutuzumab plus an indication-specific chemotherapy against the standard of care, Rituximab plus the same chemotherapy. At the time, only a preliminary analysis of a randomized Phase 2 study in one of the indications comparing monotherapy only and assessing response instead of PFS was available. We discuss how an initial assesssment of Bayesian Predictive Power (BPP) was put together in this setup, by synthesizing information on the response-PFS association and prior assumptions. We further illustrate how this initial BPP was then updated (or not) at key study or program milestones such as read-out of one of the trials in another indication or not stopping a trial at a futility or efficacy interim analyses. BPP is generally used to inform the probability of success (PoS), a number with wide-ranging impact in our company such as valuation of the pipeline, gating of decisions by senior management, planning manufacturing capacity, or allocation of resources. We conclude by sharing experiences and recommendations on how to manage and communicate PoS updating throughout an entire development program.