RESUMO
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.
Assuntos
DNA Tumoral Circulante , Variações do Número de Cópias de DNA , Aprendizado de Máquina , Neoplasia Residual , Carga Tumoral , Humanos , DNA Tumoral Circulante/genética , DNA Tumoral Circulante/sangue , Neoplasia Residual/genética , Sequenciamento Completo do Genoma , Neoplasias/genética , Neoplasias/sangue , Neoplasias/terapia , Neoplasias/patologia , Polimorfismo de Nucleotídeo Único , Biomarcadores Tumorais/genética , Biomarcadores Tumorais/sangue , Neoplasias Colorretais/genética , Neoplasias Colorretais/sangue , Neoplasias Colorretais/patologia , Neoplasias Pulmonares/genética , Neoplasias Pulmonares/sangue , Neoplasias Pulmonares/patologiaRESUMO
Single-cell RNA sequencing has revealed extensive transcriptional cell state diversity in cancer, often observed independently of genetic heterogeneity, raising the central question of how malignant cell states are encoded epigenetically. To address this, here we performed multiomics single-cell profiling-integrating DNA methylation, transcriptome and genotype within the same cells-of diffuse gliomas, tumors characterized by defined transcriptional cell state diversity. Direct comparison of the epigenetic profiles of distinct cell states revealed key switches for state transitions recapitulating neurodevelopmental trajectories and highlighted dysregulated epigenetic mechanisms underlying gliomagenesis. We further developed a quantitative framework to directly measure cell state heritability and transition dynamics based on high-resolution lineage trees in human samples. We demonstrated heritability of malignant cell states, with key differences in hierarchal and plastic cell state architectures in IDH-mutant glioma versus IDH-wild-type glioblastoma, respectively. This work provides a framework anchoring transcriptional cancer cell states in their epigenetic encoding, inheritance and transition dynamics.
Assuntos
Neoplasias Encefálicas/genética , Neoplasias Encefálicas/patologia , Plasticidade Celular/genética , Epigênese Genética , Glioma/genética , Glioma/patologia , Padrões de Herança/genética , Transcrição Gênica , Linhagem Celular Tumoral , Ilhas de CpG/genética , Variações do Número de Cópias de DNA/genética , Metilação de DNA/genética , Humanos , Isocitrato Desidrogenase/genética , Filogenia , Complexo Repressor Polycomb 2/metabolismo , Regiões Promotoras Genéticas/genética , Análise de Célula Única , Transcriptoma/genéticaRESUMO
Cancer evolution poses a central obstacle to cure, as resistant clones expand under therapeutic selection pressures. Genome sequencing of relapsed disease can nominate genomic alterations conferring resistance but sample collection lags behind, limiting therapeutic innovation. Genome-wide screens offer a complementary approach to chart the compendium of escape genotypes, anticipating clinical resistance. We report genome-wide open reading frame (ORF) resistance screens for first- and third-generation epidermal growth factor receptor (EGFR) inhibitors and a MEK inhibitor. Using serial sampling, dose gradients, and mathematical modeling, we generate genotype-fitness maps across therapeutic contexts and identify alterations that escape therapy. Our data expose varying dose-fitness relationship across genotypes, ranging from complete dose invariance to paradoxical dose dependency where fitness increases in higher doses. We predict fitness with combination therapy and compare these estimates to genome-wide fitness maps of drug combinations, identifying genotypes where combination therapy results in unexpected inferior effectiveness. These data are applied to nominate combination optimization strategies to forestall resistant disease.