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We are rapidly approaching a future in which cancer patient digital twins will reach their potential to predict cancer prevention, diagnosis, and treatment in individual patients. This will be realized based on advances in high performance computing, computational modeling, and an expanding repertoire of observational data across multiple scales and modalities. In 2020, the US National Cancer Institute, and the US Department of Energy, through a trans-disciplinary research community at the intersection of advanced computing and cancer research, initiated team science collaborative projects to explore the development and implementation of predictive Cancer Patient Digital Twins. Several diverse pilot projects were launched to provide key insights into important features of this emerging landscape and to determine the requirements for the development and adoption of cancer patient digital twins. Projects included exploring approaches to using a large cohort of digital twins to perform deep phenotyping and plan treatments at the individual level, prototyping self-learning digital twin platforms, using adaptive digital twin approaches to monitor treatment response and resistance, developing methods to integrate and fuse data and observations across multiple scales, and personalizing treatment based on cancer type. Collectively these efforts have yielded increased insights into the opportunities and challenges facing cancer patient digital twin approaches and helped define a path forward. Given the rapidly growing interest in patient digital twins, this manuscript provides a valuable early progress report of several CPDT pilot projects commenced in common, their overall aims, early progress, lessons learned and future directions that will increasingly involve the broader research community. 

Highly stretchable fiber sensors have attracted significant interest recently due to their applications in wearable electronics, human–machine interfaces, and biomedical implantable devices. Here, a scalable approach for fabricating stretchable multifunctional electrical and optical fiber sensors using a thermal drawing process is reported. The fiber sensors can sustain at least 580% strain and up to 750% strain with a helix structure. The electrical fiber sensor simultaneously exhibits ultrahigh stretchability (400%), high gauge factors (≈1960), and excellent durability during 1000 stretching and bending cycles. It is also shown that the stretchable step‐index optical fibers facilitate detection of bending and stretching deformation through changes in the light transmission. By combining both electrical and optical detection schemes, multifunctional fibers can be used for quantifying and distinguishing multimodal deformations such as bending and stretching. The fibers’ utility and functionality in sensing and control applications are demonstrated in a smart glove for controlling a virtual hand model, a wrist brace for wrist motion tracking, fiber meshes for strain mapping, and real‐time monitoring of multiaxial expansion and shrinkage of porcine bladders. These results demonstrate that the fiber sensors can be promising candidates for smart textiles, robotics, prosthetics, and biomedical implantable devices.