Biology subjects

Levesque, M. P.

Publications and source records attributed to Levesque, M. P..

10 recordsLinked to original sources

bioRxiv2026

CATFISHR: integration of transcriptome and inferred CNV profiles for comprehensive malignant cell identification

Malignant cell identification in single-cell RNA sequencing (scRNA-seq) remains challenging because transcriptomic cell typing, somatic variant detection and inferred copy-number approaches each have modality-specific limitations. Rare or ambiguous malignant populations are especially vulnerable to misclassification or exclusion, despite their relevance to treatment resistance, minimal residual disease, and metastasis. We developed CATFISHR, a semi-automated workflow that integrates RNA-expression similarity, inferred copy-number variation (CNV) profiles, and consensus cell typing to classify malignant cells relative to high-confidence references. CATFISHR was evaluated across eight cohorts, including seven scRNA-seq datasets spanning multiple carcinomas and melanoma subtypes, and one 10x Visium spatial transcriptomics sample. Compared with available benchmarks or ground truths, CATFISHR achieved balanced accuracy > 0.85 in six of eight cohorts and F1 score > 0.80 in six of eight cohorts. Cells uniquely identified by CATFISHR as malignant ("Hidden" cells) were present in every dataset and were not consistently explained by standard quality-control metrics. In several cohorts, Hidden cells were transcriptionally closer to benchmark malignant than non-malignant compartments; in Visium data, sampled Hidden spots contained histologist-confirmed morphologically malignant cells. CATFISHR reduces missed malignant calls while preserving rare, transitional and benchmark-discordant populations for downstream investigation.

bioinformatics↗

bioRxiv2026

Cell-type-resolved spatial proteogenomics from matched genome and proteome of the same cells

The genome and proteome of the same cells are rarely measured together, which is especially consequential in cancer, where somatic mutations vary across clones and drive disease. We show that a single standard proteomics extraction tip can retain peptides on-tip after digestion while genomic DNA passes into the normally discarded flowthrough. Combined with Deep Visual Proteomics, flowthrough co-isolation enables cell-type-resolved spatial proteogenomics from archival FFPE tissue, demonstrated in melanoma.

biochemistry↗

bioRxiv2025

Early overactivation of non-muscle myosin II during adaptation to combined BRAF and MEK inhibitors in dedifferentiating cutaneous melanomas

Cutaneous melanoma is a very aggressive type of skin cancer with remarkable phenotypic plasticity that contributes to adaptation and resistance to targeted therapies against the MAPK pathway. Previous research described that non-muscle myosin II (NMII) of the actomyosin cytoskeleton, which is essential for cell migration and metastasis, is overactivated in BRAF inhibitor-resistant melanomas. Since the combination of BRAF and MEK inhibitors (BMi) is the current standard of care, we investigated if and how NMII activity is regulated during adaptation to BMi. Here, we find that most dedifferentiating BMi-resistant melanomas overactivate NMII compared to their parental counterparts. NMII activity generally increases during the first 2 weeks of BMi treatment, and it is followed by elevated total NMII levels due partly to transcriptional modulation. Although ERK activity rebounds with similar kinetics, NMII overactivation is not prevented by ERK inhibition but by blockade of ROCK. In melanomas that hyperdifferentiate during adaptation to BMi, NMII activity is not increased upon BMi treatment due, in part, to MITF. We also find that co-targeting NMII along BMi in some melanomas reduces survival of drug-tolerant persister cells, which would delay the development of resistance. Therefore, our study identifies elevated NMII activity as a potential marker of adaptation to MAPK in some melanoma subpopulations, and also provide an approach to delay the emergence of resistance to MAPK-targeted therapy.

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↗

bioRxiv2023

Standardization of suspension and imaging mass cytometry readouts for clinical decision making

Suspension and imaging mass cytometry are single-cell, proteomic-based methods used to characterize tissue composition and structure. Data assessing the consistency of these methods over an extended period of time are still sparse and are needed if mass cytometry-based methods are to be used clinically. Here, we present experimental and computational pipelines developed within the Tumor Profiler clinical study, an observational clinical trial assessing the relevance of cutting-edge technologies in guiding treatment decisions for advanced cancer patients. By using aliquots of frozen antibody panels, batch effects between independent experiments performed within a time frame of one year were minimized. The inclusion of well-characterized reference samples allowed us to assess and correct for batch effects. A systematic evaluation of a test tumor sample analyzed in each run showed that our batch correction approach consistently reduced signal variations. We provide an exemplary analysis of a representative patient sample including an overview of data provided to clinicians and potential treatment suggestions. This study demonstrates that standardized suspension and imaging mass cytometry measurements generate robust data that meet clinical requirements for reproducibility and provide oncologists with valuable insights on the biology of patient tumors.

cancer biology↗

bioRxiv2022

Androgen Receptor is a Determinant of Melanoma Targeted Drug Resistance

Melanoma provides a primary benchmark for targeted drug therapy. Most melanomas with BRAFV600 mutations regress in response to BRAF/MEK inhibitors (BRAFi/MEKi). However, nearly all relapse within the first two years, and there is a connection between pathways involved in BRAFi/MEKi-resistance and poor response to immune checkpoint therapy. We recently showed that androgen receptor (AR) activity is required for melanoma cell proliferation and tumorigenesis. Here we find that AR expression is markedly increased in BRAFi resistant melanoma cells as well as in sensitive cells soon after BRAFi exposure. Increased AR expression is by itself sufficient to render melanoma cells BRAFi-resistant, eliciting transcriptional changes of BRAFi resistant subpopulations and elevated EGFR and SERPINE1 expression of likely clinical significance. Inhibition of AR expression and activity blunts changes in gene expression and suppresses proliferation and tumorigenesis of BRAFi-resistant melanoma cells, enhances MHC I expression and CD8+ T cells infiltration. Our findings point to targeting AR as a possible co-adjuvant approach for the prevention and management of the disease.

cancer biology↗

bioRxiv2022

gExcite - A start-to-end framework for single-cell gene expression, hashing, and antibody analysis

SummarySingle-cell RNA sequencing (scRNA-seq) based gene expression analysis is now an established powerful technique to decipher tissues at a single-cell level. Recently, CITE-seq emerged as a multimodal single-cell technology capturing gene expression and surface protein information from the same single-cells, which allows unprecedented insights into disease mechanisms and heterogeneity, as well as immune cell profiling. Multiple single-cell profiling methods exist, but they are typically focussed on either gene expression or antibody analysis, not their combination. Moreover, existing software suites are not easily scalable to a multitude of samples. To this end, we designed gExcite, a start-to-end workflow that provides both gene expression and CITE-seq analysis, as well as hashing deconvolution. Embedded in the Snakemake workflow manager, gExcite facilitates reproducible and scalable analyses. We showcase the output of gExcite on a study of different dissociation protocols on PBMC samples. AvailabilitygExcite is open source available on github at https://github.com/ETH-NEXUS/gExcite_pipeline The software is distributed under the GNU General Public License 3 (GPL3). Contactsinger@nexus.ethz.ch Supplementary InformationSupplementary information is available at the journals web site.

bioinformatics↗

bioRxiv2021

Multiplexed Imaging Mass Cytometry of Chemokine Milieus in Metastatic Melanoma Characterizes Features of Response to Immunotherapy

Intratumoral immune cells are crucial for tumor control and anti-tumor responses during immunotherapy. Immune cell trafficking into tumors is mediated by chemokines, which are expressed and secreted upon various stimuli and interact with specific receptors. To broadly characterize chemokine expression and function in tumors, we have used multiplex mass cytometry-based imaging of protein markers and RNA transcripts to analyze the chemokine landscape and immune infiltration in metastatic melanoma samples. Tumors that lacked immune infiltration were devoid of most chemokines and exhibited particularly low levels of antigen presentation and inflammation. Infiltrated tumors were characterized by expression of multiple chemokines. CXCL9 and CXCL10 were often localized in patches associated with dysfunctional T cells expressing CXCL13 which was strongly associated with B cell patches and follicles. TCF7+ naive-like T cells, which predict response to immunotherapy, were enriched in the vicinity of B cell patches and follicles. Our data highlight the strength of RNA and protein co-detection which was critical to deconvolve specialized immune microenvironments in inflamed tumors based on chemokine expression. Our findings further suggest that the formation of tertiary lymphoid structures is accompanied by naive and naive- like T cell recruitment, which ultimately boosts anti-tumor activity. One sentence summaryInflammatory chemokine milieus in metastatic melanoma are hotspots of T cell dysfunction and CXCL13 expression, which likely guide the recruitment of B cells and the formation of B cell follicles responsible for anti-tumor immunity.

cancer biology↗

bioRxiv2020

Sustained Androgen Receptor signaling is a determinant of melanoma cell growth potential and tumorigenesis

Melanoma is a benchmark of major clinical significance for cancer development with greater aggressiveness in the male than the female population. Surprisingly little is known on the role of androgen receptor (AR) signaling in the disease. Irrespectively of expression levels, genetic and pharmacological suppression of AR activity in a large panel of melanoma cells, derived from both male and female patients, suppresses proliferation and self-renewal potential while, conversely, increased AR expression or ligand stimulation enhance proliferation. AR gene silencing in multiple melanoma lines elicits a shared gene expression signature related to interferon- and inflammatory cytokines signaling with an inverse association with DNA repair-associated genes, which is significantly linked with better patients survival. AR plays an essential function in maintenance of genome integrity: in both cultured melanoma cells and tumors, loss of AR activity leads to chromosomal DNA breakage, leakage into the cytoplasm, and stimulator of interferon genes (STING) activation. In vivo, reduced tumorigenesis resulting from AR gene silencing or pharmacological inhibition is associated with intratumor macrophage infiltration and, in an immune competent mouse model, cytotoxic T cell activation. Although at different levels, androgens are produced in both male and female individuals and AR targeting provides an attractive therapy approach for improved management of melanoma irrespective of patients sex and gender. SignificanceThe study uncovers an essential role of androgen receptor (AR) signaling in melanoma cell expansion and tumorigenesis, with loss of AR activity inducing cellular senescence, genomic DNA breakage, a STING dependent inflammatory cascade and immune cells recruitment. Use of AR inhibitors as growth inhibitory and DNA damaging agents in melanoma cells can provide an attractive venue for new combination approaches for management of the disease.

cancer biology↗

bioRxiv2017

Improving Leukemic CD34+/CD38- Blasts Characterization With Single-Cell Transcriptome Sequencing

SUMMARY / ABSTRACTAcute myeloid leukemia (AML) is a particularly aggressive blood cancer that is difficult to treat because of the incomplete eradication of rare blast cells that possess self-renewal and leukemia-initiating properties. To characterize resistant blasts, we analyzed for the first time the transcriptomes of individual CD34+/CD38- blasts by single-cell mRNA sequencing of 359 CD33+/CD34+/CD38-/+ sorted cells from two patients with AML and four unaffected individuals. We demonstrated that the captured blasts possess the transcriptomic hallmarks of self-renewal and leukemia-initiating ability. The effects of somatic mutations on the cancer cells are visible at the transcriptional level, and the cellular signaling pathway activity of the blasts is altered, revealing disease-associated gene networks. We also identified a core set of transcription factors that were co-activated in blasts, which suggests a joint transcription program among blasts. Finally, we revealed that leukemogenesis and putative prognostic gene-expression signatures are present at diagnosis in leukemic CD33+/CD34+/CD38- cells and can be detected using a single-cell RNA sequencing approach.

cancer biology↗