Abstract PR01: ctDNA shedding dynamics dictate early lung cancer detection potential

Early cancer detection aims to find tumors before they progress to an incurable stage. Prospective studies with tens of thousands of healthy participants are ongoing to determine whether asymptomatic cancers can be accurately detected by analyzing circulating tumor DNA (ctDNA) from blood samples. We developed a stochastic mathematical model of tumor evolution and ctDNA shedding to investigate the potential and the limitations of ctDNA-based cancer early detection tests. We inferred ctDNA shedding rates of early-stage lung cancers and calculated that a 15-mL blood sample contains on average only 1.5 genome equivalents (GE) of ctDNA for lung tumors with 1 billion cells (concentration of 0.19 GE per plasma mL; tumor fraction of 0.02%). This low level of ctDNA can be explained by the relatively low turnover rate of lung cancer cells. Similarly, fast-growing tumors led to lower levels of ctDNA because a lower number of cell death events decreases the amount of released ctDNA. We designed a detection test of virtual tumors that accounts for the varying plasma DNA concentrations in cancer and cancer-free patients and that incorporates various sources for technical and biologic errors. Two important considerations for disease detection are the expected number of mutations per tumor covered by the sequencing panel and the underlying error rate of the sequencing assay. To determine the potential performance of mutation-based ctDNA detection tests, we computed receiver operating characteristic (ROC) curves and calculated area under the curve (AUC) values. In the case of early relapse detection, we assumed that the sequencing panel covers 20 known clonal mutations per primary tumor with a background error-rate of 1.5 × 10−5 per base pair. For monthly relapse testing with a specificity of 99.5%, we found a median detection size of 0.19 cm3. Moreover, we observed a lead time of 180 days compared to monthly imaging-based relapse detection. While tracking fewer than 20 mutations drastically decreased the expected lead time, tracking more than 20 mutations only minimally increased the expected lead time. Similarly, the increases in lead time gained slowed down for more frequent testing than one month for relapsing tumors with a growth rate of 1%. In the case of cancer screening, we assumed that the sequencing panel has the same error rate as above and covers 2,000 base pairs, resulting in approximately one detectable mutation per lung cancer, which is similar to the Cancer