Ultrasensitive plasma-based monitoring of tumor burden using machine-learning-guided signal enrichment.

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.

High-throughput identification of genotype-specific cancer vulnerabilities in mixtures of barcoded tumor cell lines.

Hundreds of genetically characterized cell lines are available for the discovery of genotype-specific cancer vulnerabilities. However, screening large numbers of compounds against large numbers of cell lines is currently impractical, and such experiments are often difficult to control. Here we report a method called PRISM that allows pooled screening of mixtures of cancer cell lines by labeling each cell line with 24-nucleotide barcodes. PRISM revealed the expected patterns of cell killing seen in conventional (unpooled) assays. In a screen of 102 cell lines across 8,400 compounds, PRISM led to the identification of BRD-7880 as a potent and highly specific inhibitor of aurora kinases B and C. Cell line pools also efficiently formed tumors as xenografts, and PRISM recapitulated the expected pattern of erlotinib sensitivity in vivo.

Toward performance-diverse small-molecule libraries for cell-based phenotypic screening using multiplexed high-dimensional profiling.

High-throughput screening has become a mainstay of small-molecule probe and early drug discovery. The question of how to build and evolve efficient screening collections systematically for cell-based and biochemical screening is still unresolved. It is often assumed that chemical structure diversity leads to diverse biological performance of a library. Here, we confirm earlier results showing that this inference is not always valid and suggest instead using biological measurement diversity derived from multiplexed profiling in the construction of libraries with diverse assay performance patterns for cell-based screens. Rather than using results from tens or hundreds of completed assays, which is resource intensive and not easily extensible, we use high-dimensional image-based cell morphology and gene expression profiles. We piloted this approach using over 30,000 compounds. We show that small-molecule profiling can be used to select compound sets with high rates of activity and diverse biological performance.