Comprehensive monitoring of diverse cancer outcomes using genomic and epigenomic sequencing of cfDNA - PROJECT SUMMARY Cancer monitoring is essential for the early detection and timely intervention of various adverse outcomes, such as minimal residual disease (MRD), recurrence, and side effects. Plasma cell-free DNA (cfDNA) has shown great promise for cancer monitoring due to its unique advantages: (1) noninvasiveness, which enables repeated collection; (2) comprehensiveness, as cfDNA contains DNA from different tumor clones; and (3) capability to inform side effects, as cfDNA comes from various organs, and damage to a specific organ can lead to increased DNA release from that organ. These advantages make cfDNA ideal to monitor various adverse outcomes. Yet a gap exists between the potential of cfDNA applications and method development. Current usage of cfDNA is limited to MRD/recurrence detection with small mutation panels. The proposed project aims to develop a computational system, TreatMonitor, for ultrasensitive, comprehensive, broadly applicable, and affordable cancer monitoring using multi-omics profiling of cfDNA. Dr. Li will improve MRD/recurrence monitoring by integrating exome- and methylome-wide tumor signals (Aim 1, K99). She aims to enable sensitive monitoring of side effects by accurately and comprehensively quantifying tissue contribution in cfDNA using deep learning (Aim 2a, K99), and identifying tissue damage from abnormally altered tissue contributions in patients’ cfDNA samples (Aim 2b, R00). Throughout the K99 and R00 phases, the method will be validated technically and clinically. TreatMonitor will be the first to achieve several milestones: the first approach to integrate cfDNA exome and methylome for monitoring, and the first cfDNA-based method for monitoring side effects during cancer treatment. TreatMonitor thus offers a comprehensive solution to monitor various adverse outcomes, informing early intervention, facilitating cfDNA-based cancer research, and ultimately improving the quality of life of cancer patients. Dr. Li’s’ long-term goals are to understand genetic and epigenetic influences on cancer treatment outcomes and to develop prognostic and predictive models that guide treatment selection. The overall training objective is to provide Dr. Li with additional years of mentorship to become a highly qualified independent investigator at the intersection of precision oncology, computational biology, and statistics. Training goals include the development of competencies in (1) the clinical and biological characteristics of cancer and its treatment, (2) advanced statistical methods in deep learning and longitudinal data analysis, and (3) professional development skills. Through this training, her background in statistics, computational biology, and liquid biopsy will be integrated to solidify her expertise in precision oncology as she transitions to an independent tenure-track faculty position. During the K99 phase, Dr. Li will be under the primary mentorship of Dr. Steven Dubinett, with a strong co-mentoring team (Drs. Wing Hung Wong and Samuel French). UCLA has a vibrant interdisciplinary community for cancer research with active collaboration, and all of the required equipment and facilities.
