Biology subjects

Lattmann, E.

Publications and source records attributed to Lattmann, E..

5 recordsLinked to original sources

bioRxiv2026

p63 and PITX1 sustain a pre-invasive malignant keratinocyte population in squamous cell carcinoma precursors

BackgroundCutaneous squamous cell carcinoma (cSCC) is among the most common human cancers, yet the cellular identity and molecular programs of its preinvasive precursor, actinic keratosis (AK), remains poorly defined. MethodsWe applied CITE-seq to patient-matched AK, UV-exposed normal skin, and non-UV-exposed normal skin (n=5 patients, 12 biopsies) and performed spatial whole-transcriptome profiling in an independent cohort (n=4) to map pre-invasive keratinocyte states at single-cell resolution. ResultsWe identify AK-specific keratinocytes (ASK), a discrete population localized to the dysplastic basal epidermis and characterized by UV-associated mutational signatures (SBS7b), high mutational burden, and recurrent copy number alterations including 9p loss and 8q gain. ASK occupies a basal-like undifferentiated state sustained by a {Delta}Np63/PITX1 regulatory module that attenuates Notch/HES1-driven differentiation and activates glycolytic metabolism. Comparison with published cSCC data reveals that ASK share core tumor-propagating gene networks with tumor-specific keratinocytes (TSK), including IGFBP6, IGFBP2, and ITGA6, but lack invasion effectors MMP1, MMP10, and PTHLH. Functional experiments identify IGFBP6 as a pro-proliferative factor in AK-derived keratinocytes. The AK microenvironment shows expansion of inflammatory basal keratinocytes, barrier disruption, and early immunosuppressive T cell remodeling. ConclusionsThese findings define the molecular identity of a pre-invasive malignant keratinocyte population governed by p63/PITX1 and distinguish early oncogenic programs shared with invasive cSCC from later-acquired invasion effectors, identifying candidate targets for prevention or treatment of squamous cell carcinoma.

cancer biology↗

bioRxiv2025

Deep learning-driven morphology analysis enables label-free classification of therapeutic agent- naive versus resistant cancer cells

Therapeutic drug treatments of solid tumors are often undermined by various resistance mechanisms. Identification of drug-resistance phenotypes at the single cell level is challenging because conventional molecular methods are cell-destructive, labor-intensive, and cost-prohibitive. To overcome these challenges, we developed an orthogonal approach to drug-resistance phenotyping, through the use of deep-learning-driven morphology analysis of single, high resolution cell images. Specifically, we trained deep learning-based drug resistance classifiers using cell images from 5 different cell lines that were rendered resistant to 5 different therapeutic agents, using a foundation model framework. With high accuracy, the classifier correctly predicted naive or resistance phenotypes across all cancer types and across all the therapeutic agent types (chemotherapeutic, targeted) tested. These results showed that morphology can capture complex phenotype information in the context of drug treatment. To demonstrate the potential clinical utility of the drug resistance classifier, it was applied to a dissociated tumor biopsy and the resulting phenotype predictions were in close concordance with scRNASeq analysis of the biopsy. Our study highlights the potential of deep-learning-driven morphology analysis to provide complex phenotype information, and ultimately shape oncology drug treatment strategies at the patient-level in a clinical context.

cancer biology↗

bioRxiv2024

Label-Free Melanoma Phenotype Classification Using Artificial Intelligence-Based Morphological Profiling

Melanomas are the deadliest skin cancers, in part due to cellular plasticity and heterogeneity. Intratumoral heterogeneity drives varied mutable phenotypes, specifically "melanocytic" and "mesenchymal" cell states, which result in differential functional properties and drug responses. Definitive and rigorous classification of these phenotypic states has been challenging with conventional biomarker-based methods, and high-parameter molecular methods are cell-destructive, labor-intensive, and time-consuming. To overcome these technical and practical limitations, we utilized label-free artificial intelligence-based morphological profiling to classify live melanoma cells into melanocytic and mesenchymal phenotypes based on high resolution imaging of single cells. To predict the phenotypes of single melanoma cells based on morphology alone, we developed the AI-based Melanoma Phenotype Classifier trained with 19 patient-derived cell lines with known melanocytic or mesenchymal transcriptional profiles. To link phenotypic state with high-dimensional morphological profiles, cells were subjected to genetic and chemical perturbations known to shift phenotypic states. The AI classifier successfully predicted phenotypic shifts which were confirmed by single-cell RNA-Seq (scRNA-Seq). These results demonstrate that correlations between melanoma cell phenotypes and morphological changes are detectable by AI. Additionally, the Melanoma Phenotype Classifier was applied to dissociated tumor biopsy samples and characterization of phenotypic heterogeneity was supported by scRNA-Seq transcriptional profiles. This work establishes a link between cell morphology and melanoma phenotypes, laying the groundwork for the use of a label-free morphology-based method for phenotyping live melanoma cells combined with additional analyses.

cancer biology↗

bioRxiv2021

A DNA Replication-Independent Function of the pre-Replication Complex during Cell Invasion in C. elegans

Cell invasion is an initiating event during tumor cell metastasis and an essential process during development. A screen of C. elegans orthologs of genes over-expressed in invasive human melanoma cells has identified several components of the conserved DNA pre-replication complex (pre-RC) as positive regulators of anchor cell (AC) invasion. The pre-RC functions cell-autonomously in the G1-arrested AC to promote invasion, independently of its role in licensing DNA replication origins in proliferating cells. While the helicase activity of the pre-RC is necessary for AC invasion, the downstream acting DNA replication initiation factors are not required. The pre-RC promotes the invasive fate by regulating the expression of extracellular matrix genes and components of the PI3K signaling pathway. Increasing PI3K pathway activity partially suppressed the AC invasion defects caused by pre-RC depletion, suggesting that the PI3K pathway is one critical pre-RC target. We propose that the pre-RC acts in the non-proliferating AC as a transcriptional regulator that facilitates the switch to an invasive phenotype.

cell biology↗

bioRxiv2019

The C. elegans homolog of the Evi1 proto-oncogene, egl-43, coordinates G1 cell cycle arrest with pro-invasive gene expression during anchor cell invasion

Cell invasion allows cells to migrate across compartment boundaries formed by basement membranes. Aberrant cell invasion is a first step during the formation of metastases by malignant cancer cells.\n\nAnchor cell (AC) invasion in C. elegans is an excellent in vivo model to study the regulation of cell invasion during development. Here, we have examined the function of egl-43, the homolog of the human Evi1 proto-oncogene (also called MECOM), in the invading AC. egl-43 plays a dual role in this process, firstly by imposing a G1 cell cycle arrest to prevent AC proliferation, and secondly, by activating pro-invasive gene expression. We have identified the AP-1 transcription factor fos-1 and the Notch homolog lin-12 as critical egl-43 targets. A positive feedback loop between fos-1 and egl-43 induces pro-invasive gene expression in the AC, while repression of lin-12 Notch expression by egl-43 ensures the G1 cell cycle arrest necessary for invasion. Reducing lin-12 levels in egl-43 depleted animals restored the G1 arrest, while hyperactivation of lin-12 signaling in the differentiated AC was sufficient to induce proliferation.\n\nTaken together, our data have identified egl-43 Evi1 as a critical factor coordinating cell invasion with cell cycle arrest.\n\nAuthor summaryCells invasion is a fundamental biological process that allows cells to cross compartment boundaries and migrate to new locations. Aberrant cell invasion is a first step during the formation of metastases by malignant cancer cells.\n\nWe have investigated how a specialized cell in the Nematode C. elegans, the so-called anchor cell, can invade into an adjacent epithelium. Our work has identified an oncogenic transcription factor that controls the expression of specific target genes necessary for cell invasion, and at the same time inhibits the proliferation of the invading anchor cell.\n\nThese findings shed light on the mechanisms, by which cells decide whether to proliferate or invade.

developmental biology↗