Computational Approaches to Reconstruct the Evolutionary History of Cancer Metastases

Metastasis is the leading cause of cancer-related death. Cancer development, progression and metastasis happens through an evolutionary process, selecting for tumor clones with higher growth potential, resistance to treatment and immune pressures, and increased fitness to thrive in a new environment. While the early stages of tumor evolution leading to the development of primary tumors are quite well understood, the genetic makeup and evolutionary dynamics of cancer metastases remain less explored. Deeper study of the evolution of metastatic cancer holds immense promise, potentially paving the way for interventions that could prevent as much as 90% of cancer-related deaths. The PI on this proposal, Dr. Peter Van Loo, is an expert in leveraging DNA sequencing to infer cancer’s evolutionary history. These approaches, collectively coined Molecular Archeology of Cancer, have led to important biological insight into how tumors evolve in multiple cancer types. Across cancers, they showed that intra-tumor heterogeneity is pervasive and that primary tumors evolve over many years to decades and follow somewhat ordered paths. The genomes of cancer metastases typically show higher frequencies of aneuploidy and whole-genome duplication than those of primary tumors. This both necessitates the development of molecular archeology of cancer approaches bespoke to the analysis of cancer metastases, and provides opportunities for more detailed inference. The Van Loo lab recently developed a novel chromosomal gain timing approach, GRITIC, specifically designed to time complex copy number gains in cancer metastases. Further development of these molecular archeology of cancer metastasis methods will lead to key opportunities to advance understanding of these deadly diseases. Here, we will develop novel molecular archeology of cancer approaches to elucidate the evolutionary history of cancer metastases and demonstrate their effectiveness through application to small-cell lung cancer, one of the deadliest cancer types, often diagnosed at the metastatic stage. We will achieve this in three aims: Aim 1: Develop novel clonal evolutionary timing approaches to time genomic events in metastatic cancer evolution. Aim 2: Develop novel approaches to elucidate trajectories of mutational process activity over clonal evolutionary time with fine resolution. Aim 3: Elucidate the evolutionary history of metastatic small cell lung cancer through ctDNA profiling and research autopsy. Project Number: 1R01CA299449-01A1 | Fiscal Year: 2026 | NIH Institute/Center: National Cancer Institute (NCI) | Principal Investigator: Peter Van Loo | Institution: UNIVERSITY OF TX MD ANDERSON CAN CTR, HOUSTON, TX | Award Amount: $688,073 | Activity Code: R01 | Study Section: Biodata Management and Analysis Study Section[BDMA] View on NIH RePORTER: https://reporter.nih.gov/project-details/11294410