Marc Ladanyi

This study identifies RRASQ87L and RRAS2Q72L mutations as recurrent oncogenic drivers in lung cancer and shows sensitivity to the pan-RAS inhibitor RMC-6236.

The paper develops Mosaic, a machine learning system that classifies tumors into detailed subtypes and identifies molecular phenotypes using histopathologic and genomic data from a large patient cohort.

This study identifies APOBEC3 mutagenesis as a common driver of therapy resistance in breast cancer, associated with poorer clinical outcomes.

The paper uses machine learning to identify cancer driver mutations, finding that methods incorporating protein structure or functional genomic data outperform those using only evolutionary data, and validates their predictions using real-world patient cohorts.

The paper identifies how germline and somatic mutations interact to cause resistance to CDK4/6 inhibitors in breast cancer and proposes a sequential treatment strategy using PARP inhibitors.

This study presents a paired tumor:normal sequencing dataset for hematologic malignancies, showing improved identification of somatic variants and their clinical relevance.

MiMSI improves MSI detection in low tumor purity samples using deep learning and multiple instance learning, achieving higher sensitivity and auROC than existing methods.

The paper describes the development and clinical application of MSK-ACCESS, a liquid biopsy assay that improves the specificity of somatic variant detection in cell-free DNA by using paired normal sequencing.

The paper shows oncologic therapy increases clonal hematopoiesis with DDR mutations, which predicts risk of therapy-related myeloid neoplasms.

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

This study evaluates the use of a molecular tumor board to interpret genomic profiling in pediatric oncology cases and provides therapeutic recommendations.