From input to impact: embedding PPI in computational ovarian cancer research.

Public and Patient Involvement (PPI) is increasingly recognised as a valuable component of health research, yet there are no published examples of its implementation in computational biology. In this study, we detail how we embedded the perspectives of those affected by ovarian cancer into a biomarker discovery project. Throughout this process, PPI partners gave practical guidance on prioritizing research areas, developing study materials and contributing as co-authors on both patient information leaflets and this publication. To ensure this involvement was meaningful and inclusive, we prioritized three key strategies. Firstly, the research team placed significant emphasis on flexibility in order to facilitate accessible participation. We offered online and in-person formats, utilised explanatory videos, scheduled around PPI team availability, and held 1-to-1 “catch-up” sessions for new members. This emphasis on flexibility enabled PPI partners to contribute to the project on their own terms and helped to build trust with the research team. Secondly, we came up with ways to value PPI contributions beyond monetary reimbursement. PPI partners were compensated for their time and expenses; however, we also supported personal learning goals through “journal club” videos and interactive sessions with other researchers. These methods of appreciation helped to promote the individual goals of each contributor and ensure that the collaboration was mutually beneficial for both researchers and PPI partners. Finally, and importantly, we acted on the feedback received from our PPI partners and reported what changes we had made to our project. Input from PPI partners informed the selection of liquid biopsy types, widened the breath of the project to include multiple subtypes of ovarian cancer and influenced the development of patient information leaflets. When combined, these three tactics produced an inclusive PPI process that improved the relevance, applicability, and acceptability of the biomarker discovery study. While these principles are broadly applicable across PPI initiatives in general, we provide reflections on their importance in addressing the challenges of PPI in a computational context. In addition to offering a guide for researchers looking to develop more responsive and cooperative PPI research practices, our experience demonstrates the potential for impactful patient partnership in computational biology.