New single-cell genomics analysis approach provides direct information on cell cycle and proliferation

SPRINTER is a new method that infers proliferation rates from single-cell genomic data, shedding light on tumor progression

In a recent study published in Genetics of Naturea large group of researchers, including members of the TRACERx and PEACE consortia, designed an algorithm called Monocyte Proliferation Rate Inference in Inhomogeneous Tumors via Evolutionary Pathways, or SPRINTER, for the analysis of monocyte genomic data, focusing on the proliferative behavior of cancer cells. The study also investigated variations in cell proliferation between genetically distinct tumor clones.

About the study

In the present study, researchers used the SPRINTER algorithm to investigate cancer cell proliferation at the single-cell level. The research also combined advanced sequencing techniques to explore the interplay between genetic mutations, cell cycle dynamics and cancer progression, providing insights into metastatic potential.

SPRINTER uses whole-genome sequencing data of a cell to sort cancer cells into distinct phases of the cell cycle, such as S phase and G2 phase, and assigns them to specific genetic clones. The approach involved several methodological innovations to overcome the limitations of existing techniques.

The study was based on single-cell deoxyribonucleic acid (DNA) sequencing (scDNA-seq) data and focused on replication timing, which is an indicator of when specific genomic regions are copied during the cell cycle. SPRINTER uses a specialized method of adjusting for errors caused by DNA replication, allowing it to accurately measure cell activity. It looks at parts of the DNA that are copied early or late and uses this information to sort and define active cells (S phase).

The researchers explained that the process involves six steps: recognizing replication patterns, analyzing changes in DNA structure, identifying active cells, grouping similar cells into clones, matching cells to clones after correcting for replication effects, and identifying other active cells (G2 phase) . This helps map how fast different groups of cancer cells are growing.

The study focused on non-small cell lung cancer and confirmed the accuracy of the SPRINTER by comparing its findings with other tests such as imaging and Ki-67 staining. SPRINTER was also tested in breast and ovarian cancer to determine whether it would perform well with different cancers. The study combined statistical analyzes and evolutionary mapping to explore the relationships between cell growth, genetic changes and metastatic ability.

Important findings

The study found that cancer proliferation rates varied significantly between tumor clones, and SPRINTER identified clones with high proliferation as having greater metastatic potential. These findings were consistent across primary and metastatic tumor samples in the non-small cell lung cancer dataset. The algorithm also revealed that highly proliferative clones tend to shed more circulating tumor DNA (ctDNA), which is a marker linked to cancer progression.

Furthermore, the ability of SPRINTER to resolve proliferation heterogeneity within tumors demonstrated that distinct clones at both primary and metastatic sites have unique growth patterns. For example, clones associated with metastasis often had increased proliferation rates compared to others. This heterogeneity was ignored in bulk estimation methods, underscoring the accuracy of SPRINTER in distinguishing proliferative behaviors.

In the breast and ovarian cancer datasets, SPRINTER showed that highly proliferative clones contained increased rates of genomic mutation, including single nucleotide variants, structural variants, and copy number changes. These findings supported the hypothesis that rapid cell division contributes to the accumulation of genomic changes.

In addition, SPRINTER also associated changes in replication timing with changes in gene expression, especially in genes involved in proliferation and metastasis. Such changes were more pronounced in highly proliferative clones, indicating a mechanistic link between non-genetic factors and aggressive cancer behaviors.

conclusions

In summary, the study showed that tumor proliferation is highly heterogeneous and driven by genetic and non-genetic factors. Detailed analysis using the SPRINTER algorithm revealed that highly proliferative clones are critical for understanding cancer metastasis and progression.

Furthermore, the study showed that these clones exhibit unique genomic alterations and increased shedding of ctDNA, providing potential biomarkers for clinical applications. Overall, the study demonstrated that SPRINTER offers a powerful framework for studying cancer progression, paving the way for targeted therapeutic strategies based on clone-specific proliferation dynamics.