Abstract 39: Real-time spatial and temporal monitoring on tumor subclonal evolution
Authors: Gabor Marth, Yi Qiao, Dillon Lee, Xiaomeng Huang
Journal: Clinical Cancer Research (2020)

Abstract Many types of cancer are found to exhibit a high level of intrasample genetic heterogeneity, as a result of the microevolution process. As genomic aberrations accumulate within tumor cells, a subset of the cells may gain a selective advantage and form an outgrowth within the tumor. When chemotherapeutic agents are administered to the patient, these tumor subclones may respond differently to the treatment and may eventually cause relapse. Identifying resistant subclones, and their genotypes, after each chemotherapy would yield valuable insights into how the tumor population reacted to the previously administered chemo-agent, and how the treatment plan should be adjusted based on the genetic features of surviving subclones. Similarly, understanding the relationships between the primary and metastatic tumor cell populations would provide knowledge about the mechanisms driving, and potentially preventing, metastasis. Our method (SubcloneSeeker) is able to reconstruct subclone structures jointly across all samples (temporal or spatial), using somatic variants jointly clustered with their Cell Prevalence (CP), to infer a unified evolutionary structure. When more than one structure is able to explain the observed somatic variants and their CP values, we can compute a consensus structure that captures the most important features (i.e., which subclones and the corresponding genomic aberrations are responsible for the resistance of a certain chemotherapy drug), as well as suggest a panel of variants to be profiled with single-cell assay in order to reduce computational ambiguity. We applied this method to several datasets, including a breast cancer patient who had received three regimens of chemotherapy and had tumor samples taken at 4 consecutive time points. Our analysis revealed that the tumor population reacted to these treatment in two distinct patterns: 1) although a significant reduction in tumor burden was observed, most of the major subclones survived and expanded again after treatment, indicating that the treatment was effective, but terminated prematurely; 2) a single subclone with extra mutations (e.g., FBXL19, a tumor suppressor by inhibiting E-cadherin downregulation) survived the treatment and became the founding clone for the tumor population post-treatment, and the extra mutations were fixed at very high CP (~100%), indicating that these extra mutations are most likely to be causative for chemoresistance. We also applied our method to breast cancer patient rapid-autopsy datasets with 28 tumor samples. Our method was able to map out subclone evolution across multiple metastatic sites and highlight variants unique to each evolutionary lineage. In conclusion, our method is able to elucidate the dynamics of tumorigenesis, drug response, and metastasis by reconstructing unified subclone evolutionary structures, allowing more informed decisions to be made in adjusting treatment plans in real time and potentially facilitating personalized cancer medicine. Citation Format: Gabor Marth, Yi Qiao, Dillon Lee, Xiaomeng Huang. Real-time spatial and temporal monitoring on tumor subclonal evolution [abstract]. In: Proceedings of the AACR Special Conference on Advancing Precision Medicine Drug Development: Incorporation of Real-World Data and Other Novel Strategies; Jan 9-12, 2020; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(12_Suppl_1):Abstract nr 39.