Abstract Shared Resource 03: Biostatistics & Mathematical Oncology (BMO-SR) The overall objective of the City of Hope Comprehensive Cancer Center (COHCCC) is to foster and integrate cancer research activities, thereby ensuring the successful forward and reverse translation of discoveries across the translational science spectrum, including prevention, control, and population science research. To help fulfill this objective, and maximize the impact of research activities, the Biostatistics and Mathematical Oncology Shared Resource (BMO-SR) works collaboratively with all COHCCC Members to provide essential and continuous biostatistical, mathematical, epidemiological, bioinformatics, and computational biology support, with tailored guidance given to each program/project. Specific Aims of the BMOC-SR: Aim 1. (Design and Analysis): Provide biostatistical, mathematical modeling, and computational biology expertise to enhance research activities. Aim 2. (Consultation/Collaboration (2A) and Training/Service (2B)): Provide consultation, collaboration, training, and regulatory support service to facilitate the advancement of cancer research and novel therapeutics. The BMO-SR, comprised of 25 faculty/staff, provides COHCCC Members (e.g., basic scientists, clinical investigators, behavioral scientists, epidemiologists involved in cancer research) with access to a group of specialized biostatisticians, mathematicians, and computational biologists who collectively possess a diverse set of skills and expertise. Access to and collaborations with BMO-SR faculty are facilitated by: i) BMO-SR alignment with the disease/modality research team organizational structure and ii) educational, training and mentorship activities available to COHCCC Members (e.g., courses, virtual and walk-in BMO-SR clinics, statistical workshops, and seminars). The overall objective of BMO-SR is to ensure that pre-clinical studies, basic science experiments, clinical trials, mathematical/machine learning models, and observational/population studies are: i) designed in an efficient and rigorous manner, ii) that high-quality data are collected, and iii) that unbiased data analysis methods are employed, such that the reported results are valid, robust, and reproducible. Members Utilization by %Revenue 2017–21: 98.3 Total (3.1 MCBC, 13.6 DCT, 27.5 CI, 50.4 HM, 3.7 CCPS) Publications by Members: 178, 37 with Impact Factor >10 Grants Supported: 72 Total (2 ACS, 9 CIRM, 5 DoD, 2 LLS, 41 NCI of 49 NIH (33R01, 1U01))