Worldwide Innovative Network Consortium: Building a Common Global Cancer Database.
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Farahnak Farhood, El-Deiry Wafik S, Lussier Yves A, Kurzrock Razelle, Magidi Shai, Bresson Catherine, Enger Shirin A, Liu Jia, Bar Jair, Warner Jeremy L, Meissner Tobias, Rubin Eitan, Rueter Jens, Gaddipati Himabindu, Kulkarni Mandar, Chen Zhen, Limaye Sewanti, Elsey Rachel, Rubenstein Brenda M, Joshua Anthony M, Al-Shamsi Humaid O, Musallam Khaled M, Wunder Fanny, Raynaud Jacques, Berchem Guy, Gantenbein Manon, Al Omari Amal, Dermime Said, Abdel-Razeq Hikmat, Saintigny Pierre, Cervantes Andrés, Reddel Roger R, Aref Adel T, Martin-Liberal Juan, Lázaro Conxi, Romera David Cordero, Sekacheva Marina, Berger Raanan, Pramesh C S, Berindan-Neagoe Ioana, Girda Eugenia, Piris-Gimenez Alejandro, Farhangfar Carol J, Salem Mohammed E, Dienstmann Rodrigo, Salazar Ramon, Frankel Naftali Z, Batist Zachary, Quintana Yuri, Batist Gerald
Аннотация
This review shares the ongoing work of the global Worldwide Innovative Network (WIN) Consortium for Precision Medicine to synthesize emerging cancer treatment data and to define the requirements for a common global cancer database that can truly support precision oncology. We performed a narrative review of emerging cancer treatment data, molecular profiling technologies, and existing clinicogenomic databases, focusing on how tumors are characterized, how subgroups are defined, and how demographic, lifestyle, and environmental factors are captured. The growth in molecular profiling technologies and the development of new targeted therapies are transforming cancer care. Tumors, regardless of tissue origin, are increasingly defined as composites of multiple, often rare, subgroups, each with distinct biology and likely response to specific therapies, based on multidimensional profiling of the tumor and its microenvironment. The solution lies in building vast databases that capture racial and ethnic diversity, reflected in genomic data, as well as diet and lifestyle factors that may have epigenetic impact on gene expression and post-translational modifications. A truly inclusive and informative data set must reflect global diversity, and there are multiple examples of demography-dependent differences in genomic signals. With members caring for and studying patients with cancer across five continents, WIN is actively exploring pathways to create a global cancer database, rich in clinical and molecular detail, granular enough for precise analysis, and large enough to power artificial intelligence-driven insights, provided appropriate data quality, validation, and governance frameworks are in place. This review surveys the current landscape and outlines practical paths forward to achieve this goal.