Meenhard Herlyn

This study identifies TFAP2A as a driver of BRAF/MEK inhibitor resistance and immunosuppression, showing its disruption enhances antitumor immunity and overcomes drug resistance.

This study uses single-cell lineage tracing to map clonal and transcriptional dynamics in melanoma metastasis, identifying distinct transcriptional programs and spatial organization patterns.

This study identifies drug-tolerant persister states in melanoma using MeRLin, showing their role in resistance and tumor recurrence.

The paper identifies the MYC-ATF4-ASS1 axis in melanoma that controls arginine synthesis and modulates immune responses, showing therapeutic potential with arginine depletors and BRAF/MEK inhibitors.

This study develops DR5-targeted CAR-T cells that kill solid tumors and MDSCs, enhance CD8+ T cell activity, and show minimal toxicity in preclinical models.

The paper shows that therapy-resistant cancer cell clones have distinct drug sensitivity profiles, and combinations targeting these subpopulations can effectively eliminate resistant populations.

SpaceBar enables clone tracing and spatial transcriptomics profiling by using synthetic barcodes to label cells and analyze gene expression in situ.

This study uses spatial transcriptomics and deep learning to analyze melanoma treatment resistance, identifying persister states and resistance mechanisms with spatiotemporal resolution.

This study identifies molecular mechanisms of resistance to immune checkpoint blockade in metastatic melanoma using proteogenomic and spatial analyses of tumor and immune microenvironment data.

The paper shows melanoma cells develop therapy resistance through AP-1-mediated cellular memories that encode transient gene expression and chromatin changes.

The paper develops a Nextflow pipeline for processing Visium spatial transcriptomics and H&E images from PDX samples, enabling multi-modal analysis and integration of spatial gene expression and morphometric features.

The paper presents a pan-cancer PDX histology image repository with genomic and pathological annotations for deep learning analysis of tumor regions and xenograft-transplant lymphoproliferative disorder prediction.

The paper shows that resistant fates in cancer cells are predetermined by intrinsic molecular differences, not microenvironmental factors, and can be influenced by drug type and dose.

The paper compiles protein response data from cancer cell lines to drug perturbations, showing improved drug sensitivity prediction and building a protein-drug connectivity map with an accessible data portal.