bioRxiv Science⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/517839

Cancer Classification from Healthy DNA using Machine Learning

Jain, S.·Mazaheri, B.·Raviv, N.·Bruck, J.
View original source ↗Export metadata ↓

Abstract

The genome is traditionally viewed as a time-independent source of information; a paradigm that drives researchers to seek correlations between the presence of certain genes and a patients risk of disease. This analysis neglects genomic temporal changes, which we believe to be a crucial signal for predicting an individuals susceptibility to cancer. We hypothesize that each individuals genome passes through an evolution channel (The term channel is motivated by the notion of communication channel introduced by Shannon1 in 1948 and started the area of Information Theory), that is controlled by hereditary, environmental and stochastic factors. This channel differs among individuals, giving rise to varying predispositions to developing cancer. We introduce the concept of mutation profiles that are computed without any comparative analysis, but by analyzing the short tandem repeat regions in a single healthy genome and capturing information about the individuals evolution channel. Using machine learning on data from more than 5,000 TCGA cancer patients, we demonstrate that these mutation profiles can accurately distinguish between patients with various types of cancer. For example, the pairwise validation accuracy of the classifier between PAAD (pancreas) patients and GBM (brain) patients is 93%. Our results show that healthy unaffected cells still contain a cancer-specific signal, which opens the possibility of cancer prediction from a healthy genome.

Explore related subjects

genomicsPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Jain, S., Mazaheri, B., Raviv, N., Bruck, J.. 2019-01-11. Cancer Classification from Healthy DNA using Machine Learning. https://doi.org/10.1101/517839

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

Genome-wide diversity across 130 years reveals drastic post-bottleneck genetic erosion in a recovering population of golden eagles (Aquila chrysaetos)

Many wild populations have experienced severe human-induced declines, but the genetic consequences of such bottlenecks are often poorly understood. In Finland, as well as in many other countries, golden eagles were heavily persecuted during the 19th-20th centuries, disappearing from southern regions and reaching their lowest population size in the 1950-1960s. Although conservation efforts facilitated demographic recovery, the long-term genomic consequences of the bottleneck remained unknown. Here, we used whole-genome sequencing data spanning 136 years to evaluate patterns of genetic diversity in 26 Finnish golden eagles before, during, and after the mid-20th century bottleneck. We found substantial loss of genetic variation, including an 82% reduction in genome-wide heterozygosity, a 42% decline in nucleotide diversity, a 67% reduction in Watterson's theta, and a substantial loss of variable sites. Analyses of the site frequency spectrum and Tajima's D confirmed historical population contraction, and runs of homozygosity analysis indicated elevated post-bottleneck inbreeding. These results indicate strong genetic erosion in Finnish golden eagles despite the census size recovery. Therefore, incorporating genomic indicators in conservation monitoring and performing regional assessments are necessary to guide management and preserve biodiversity in line with the Global Biodiversity Framework's goal of maintaining genetic variation.

genomics↗
bioRxiv2026

Long-read analysis of complete human 5S rDNA arrays reveals a zoned organization with two-state unit methylation linked to chromatin architecture

Despite the fundamental importance of the 5S ribosomal DNA (rDNA) to all cellular life, the landscape of its units by position along its array has remained poorly understood. Here, recent telomere-to-telomere long-read sequencing is used to resolve 5S rDNA arrays from flank to flank and to follow the state of each unit along single molecules. The array is zoned at both the sequence and the epigenetic level. Notably, methylation is bimodal at the level of the unit, and the combination of low-methylation and high-methylation units gives rise to the overall methylation pattern of the array. Moreover, the average number of low-methylation units remains relatively constant regardless of array length. Chromatin fiber sequencing (Fiber-seq) data further show that low-methylation units are more accessible, have lower nucleosome occupancy and are more frequently footprinted at the TFIIIB-binding site, consistent with analyses of ATAC-seq and ChIP-seq data from cell lines. Across species, this pattern is shared among apes, whereas other species show diverse patterns. These results provide a unit-resolved view of the human 5S rDNA array and reveal a zone-specific organization of unit methylation that is associated with chromatin architecture.

genomics↗
bioRxiv2026

Using Oyster Genomics to Understand Human Health

As sessile filter feeders that experience continuous environmental exposure, oysters, particularly Crassostrea virginica, offer a unique opportunity to understand how environmental exposure alters the genome and influence health. Furthermore, oysters must be sorted by size for market value for premium size and taste. However, it requires intensive labor, specialized equipment (tumblers), and is susceptible to environmental risks like pollution. Therefore, it is desirable to produce uniform oysters without machines. However, limitations in the previous reference genome [2] have constrained our understanding of how genes are organized and maintained in oyster populations. To this end, we harvested 240 adult oysters in same location, 120 extremely large size and 120 extremely small, controlled with all confounding factors such as age and water temperature. We perform a genome wise associate study (GWAS) using next generation whole genome sequencing, aiming to discover genomic regions associated with oyster size. Using PacBio HiFi, Illumina, and Hi-C technologies, we generated a 99.7% complete chromosome-level genome assembly for C. virginica (NCBI RefSeq GCF_053477285.1) [75]. We identified a ~13 Mb region on chromosome 1 uniquely expanded in C. virginica and enriched for innate immune and stress-response genes. To evaluate whether these structural features are reflected at the population level, we then conducted a k-mer analysis. This revealed differential sequence patterns by size, including enrichment of bacterial DNA in smaller individuals and host variation at the RNF213 locus within this region. By highlighting genome structure as a mediator of immune resilience, this work supports the use of oysters as sentinels to advance out understanding of how the dynamic reassembly of the genome due to environmental exposures influence health in human populations.

genomics↗