J. M. McFarland

This paper demonstrates the activation of a protodrug, SQP22, using click chemistry with tumor-targeting agents, showing anti-tumor effects in murine models with reduced toxicity.

This study compares CRISPR-Cas9 and RNAi screens to identify cancer vulnerabilities, finding that RNAi better detects pan-dependencies while CRISPR identifies more dependencies overall.

The paper identifies XPR1 and KIDINS220 as therapeutic targets in ovarian cancer by showing their role in phosphate dysregulation leading to cell death.

This study identifies a transitional cancer cell state in metastatic pancreatic cancer and shows that tumor microenvironment signals drive transcriptional plasticity and subtype transitions.

This study identifies aneuploid cancer cells as more vulnerable to spindle assembly checkpoint (SAC) inhibition and explores the underlying mechanisms.

The paper develops an unsupervised alignment method to compare tumor and cell line transcriptional profiles, revealing differences in similarity across cancer types and identifying cell lines with distinct transcriptional states.

The paper introduces MIX-Seq, a method for multiplexed single-cell transcriptional profiling of cancer cell responses to perturbations, revealing drug mechanisms and predicting cell viability.

The paper uses single-cell RNA-seq to analyze cancer cell lines and identifies recurring expression programs of heterogeneity that mirror those in tumors, suggesting intrinsic plasticity contributes to intra-tumoral diversity.

The paper identifies non-oncology drugs with anti-cancer activity across 578 cancer cell lines using PRISM, finding that many drugs selectively inhibit cancer cells based on molecular features.

This study shows that PDX tumors acquire new genomic changes over passages, differ from human tumors, and affect drug responses.