Fluctuating DNA methylation tracks cancer evolution at clinical scale

Area of Science:

• Oncology
• Genomics
• Bioinformatics

Background:

• Cancer progression is an evolutionary process, but tracking its dynamics at scale is difficult.
• Existing methods for analyzing cancer evolution are often complex or limited in scope.

Purpose of the Study:

• To develop a novel methodology, EVOFLUX, for quantitatively inferring cancer evolutionary dynamics.
• To apply EVOFLUX to a large cohort of lymphoid cancer samples to characterize evolutionary heterogeneity.
• To explore the clinical implications of cancer evolutionary dynamics, including prognosis.

Main Methods:

• Development of EVOFLUX, a method utilizing natural DNA methylation barcodes.
• Application of EVOFLUX to 1,976 bulk tumor methylation profiles from lymphoid cancers.
• Phylogenetic analysis of specific cancer subtypes, including Richter-transformed CLL.
• Orthogonal verification using genetic data (e.g., nanopore sequencing) and clinical variables.

Main Results:

• EVOFLUX quantitatively infers tumor growth rate, malignancy age, and epimutation rates from methylation data.
• Significant variation in evolutionary parameters observed across different lymphoid cancer types.
• Subclonal selection is infrequent; multiple independent primary tumors detected occasionally.
• Faster initial tumor growth correlates with aggressive disease subtypes.
• Cancer evolutionary history is a significant independent prognostic factor in chronic lymphocytic leukemia.
• EVOFLUX identified that the origin of transformed clones in Richter-transformed CLL can predate clinical presentation by decades.

Conclusions:

• Widely available DNA methylation data can precisely measure cancer evolutionary dynamics using EVOFLUX.
• EVOFLUX provides novel insights into cancer biology, heterogeneity, and clinical behavior.
• The methodology offers a low-cost, scalable approach for cancer evolutionary research and clinical application.