Seq2Karyotype (S2K): A Method for in-silico Karyotyping Using Single-Sample Whole-Genome Sequencing Data.

Area of Science:

• Genomics
• Cancer Biology
• Bioinformatics

Background:

• Cytogenetic imaging, like karyotyping, is crucial for cancer diagnosis and prognosis.
• Current methods rely on visual inspection of DNA abnormalities at single-cell resolution.
• Analyzing whole-genome sequencing data for karyotype information presents a computational challenge.

Purpose of the Study:

• To develop an in-silico tool, Seq2Karyotype (S2K), for karyotyping using unpaired whole-genome sequencing data.
• To estimate clonality and identify copy number variations (CNVs) from bulk tumor samples.
• To visualize and refine karyotype models through user-guided analysis.

Main Methods:

• Seq2Karyotype (S2K) was developed to model karyotypes and estimate clonality.
• The tool utilizes read-depth and allelic imbalance from bulk sequencing data.
• Visualization-guided refinement was incorporated for model accuracy.

Main Results:

• Analysis of 19 cancer cell lines revealed significant intratumoral heterogeneity, including whole-genome duplication events.
• Copy number variations (CNVs) were identified and validated using imaging and single-cell omics.
• High concordance with clinical cytogenetic reports was observed for acute myeloid leukemia samples.
• Evolutionary trajectories in metastatic neuroblastomas were elucidated, suggesting reversion mechanisms.

Conclusions:

• Seq2Karyotype (S2K) effectively identifies extensive and dynamic intratumoral heterogeneity driven by CNVs.
• The findings in cell lines and patient samples highlight the importance of CNV in tumor evolution.
• This tool can advance research into tumor adaptation under selective pressures, such as drug exposure.