Michael S. Lawrence

The paper identifies conserved and species-specific gene clusters coordinating disparate biological processes across eukaryotes, revealing regulatory proximity as a mechanism for transcriptional coherence.

The paper identifies oxidative protein states as drivers of metastatic fitness in lung cancer by linking ROS-regulated protein modifications to mitochondrial function and metastasis.

The paper introduces MuTopia, a framework that identifies topographic archetypes of mutagenesis in cancer genomes, revealing that mutational processes are constrained by replication timing and chromatin state.

This study identifies SIRT5-induced metabolic changes as a driver of chemoresistance in TNBC and reveals ATR checkpoint dependence as a therapeutic vulnerability.

This study creates a biliary tract cancer cell line atlas using multi-omics data to identify molecular subtypes and therapeutic targets.

This study uses high-throughput microfluidic technology to enrich CTCs from large blood volumes, enabling detailed molecular and morphological analysis of tumor cells in metastatic cancer patients.

DrugMap quantitatively analyzes cysteine ligandability across 416 cancer cell lines, revealing redox state, conformational changes, and mutations as drivers of variability, and identifies covalent inhibitors targeting NFκB1 and SOX10.

The paper shows that the structural similarity index from CHESS is unreliable due to noise in Hi-C data, warning against its biological interpretation.

The paper identifies a nucleus-to-mitochondria ROS sensing pathway involving CHK1 and SSBP1 that mediates resistance to platinum-based chemotherapies in ovarian cancers.

This study shows that APOBEC mutagenesis occurs in both tumor and viral genomes in HPV+ OPSCC, suggesting a direct link between viral restriction and host mutations.

The paper introduces the Cancer LncRNA Census (CLC), a curated resource of lncRNAs with causative roles in cancer, and shows their genomic and evolutionary features.

ActivePathways integrates multivariate omics data to identify enriched pathways, revealing cancer driver genes and prognostic pathways in breast cancer.

This paper shows that many cancer hotspots are passengers, not drivers, and introduces a new model to better identify true driver mutations.

The paper analyzes non-coding mutations across 2,583 cancer genomes to identify genes and pathways affected, finding clusters of interacting proteins and insights into cancer mechanisms.

The paper identifies and characterizes coding and non-coding driver mutations across 2,500 cancer genomes, revealing new candidates and questioning previous findings.

The paper identifies 685 putative cancer-related genes by analyzing mutation depletion in protein interaction networks across 4,700 cancer genomes.

This study characterizes genomic, transcriptomic, and epigenomic artifacts in FFPE tissues and provides best practices to mitigate their impact on molecular analysis.

The paper quantifies network mutation burdens across 21 tumor types to identify putative cancer genes, finding 62 new candidates with greater sensitivity for low-frequency mutations.