Machine Learning Reveals Molecular Similarity and Fingerprints in Structural Aberrations of Somatic Cancer

Abstract

Structural aberrations (SA) have been shown to play an essential role in the occurrence and development of cancer. SAs are typically characterized by copy number alteration (CNA) dose and distortion length. Although sequencing techniques and analytical methods have facilitated the identification and cataloging of somatic CNAs, there are no effective methods to quantify SA considering the amplitude, location, and neighborhood of each nucleotide in each fragment. Therefore, a new SA index based on dynamic time warping is proposed. The SA index analysed 22448 samples of 35 types/subtypes of cancers. Most types had significant differences in SA levels ranging between 12p and 20q. This suggests that genes or inter-gene regions may warrant greater attention, as they can be used to distinguish between different types of cancers and become targets for specific treatments. SA indexes were then used to quantify the differences between cancers. Additionally, SA fingerprints were identified for every cancer type. Kidney chromophobe, adrenocortical carcinoma, and ovarian serous cystadenocarcinoma are the three severest types with structural aberrations caused by cancer, while thyroid carcinoma is the least. Our research provides new possibilities for the better utilization of chromosomal instability for further exploiting cancer aneuploidy, thus improving cancer therapy.