ABSTRACT
In solid tumor oncology, circulating tumor DNA (ctDNA) is poised to transform care through accurate assessment of minimal residual disease (MRD) and therapeutic response monitoring. To overcome the sparsity of ctDNA fragments in low tumor fraction (TF) settings and increase MRD sensitivity, we previously leveraged genome-wide mutational integration through plasma whole-genome sequencing (WGS). Here we now introduce MRD-EDGE, a machine-learning-guided WGS ctDNA single-nucleotide variant (SNV) and copy-number variant (CNV) detection platform designed to increase signal enrichment. MRD-EDGESNV uses deep learning and a ctDNA-specific feature space to increase SNV signal-to-noise enrichment in WGS by ~300× compared to previous WGS error suppression. MRD-EDGECNV also reduces the degree of aneuploidy needed for ultrasensitive CNV detection through WGS from 1 Gb to 200 Mb, vastly expanding its applicability within solid tumors. We harness the improved performance to identify MRD following surgery in multiple cancer types, track changes in TF in response to neoadjuvant immunotherapy in lung cancer and demonstrate ctDNA shedding in precancerous colorectal adenomas. Finally, the radical signal-to-noise enrichment in MRD-EDGESNV enables plasma-only (non-tumor-informed) disease monitoring in advanced melanoma and lung cancer, yielding clinically informative TF monitoring for patients on immune-checkpoint inhibition.
Subject(s)
Circulating Tumor DNA , DNA Copy Number Variations , Machine Learning , Neoplasm, Residual , Tumor Burden , Humans , Circulating Tumor DNA/genetics , Circulating Tumor DNA/blood , Neoplasm, Residual/genetics , Whole Genome Sequencing , Neoplasms/genetics , Neoplasms/blood , Neoplasms/therapy , Neoplasms/pathology , Polymorphism, Single Nucleotide , Biomarkers, Tumor/genetics , Biomarkers, Tumor/blood , Colorectal Neoplasms/genetics , Colorectal Neoplasms/blood , Colorectal Neoplasms/pathology , Lung Neoplasms/genetics , Lung Neoplasms/blood , Lung Neoplasms/pathologyABSTRACT
Adaptive evolution may be influenced by canalization, the buffering of developmental processes from environmental and genetic perturbations, but how this occurs is poorly understood. Here, we explore how gene expression variability evolves in diverging and hybridizing populations, by focusing on the Arctic charr (Salvelinus alpinus) of Thingvallavatn, a classic case of divergence between feeding habitats. We report distinct profiles of gene expression variance for both coding RNAs and microRNAs between the offspring of two contrasting morphs (benthic/limnetic) and their hybrids reared in common conditions and sampled at two key points of cranial development. Gene expression variance in the hybrids is substantially affected by maternal effects, and many genes show biased expression variance toward the limnetic morph. This suggests that canalization, as inferred by gene expression variance, can rapidly diverge in sympatry through multiple gene pathways, which are associated with dominance patterns possibly biasing evolutionary trajectories and mitigating the effects of hybridization on adaptive evolution.
