Biology subjects

Labots, M.

Publications and source records attributed to Labots, M..

5 recordsLinked to original sources

bioRxiv2026

SIGLEC1 FACILITATES MACROPHAGE-CD8+ T CELL INTERACTIONS AND CORRELATES WITH CANCER IMMUNOTHERAPY RESPONSE

Antigen-presenting cell (APC) interactions with cytotoxic T cells are critical for anti-tumour immunity and response to immune checkpoint blockade (ICB), yet context-specific regulators in the tumour microenvironment remain not fully defined. Here we identify the lectin receptor SIGLEC1 as a key mediator of macrophage-T cell interactions in human melanoma. In a well-characterized patient cohort, SIGLEC1 was selectively upregulated in inflammatory macrophages physically associated with activated/exhausted CD8 T cells. Imaging and functional analyses revealed that SIGLEC1 accumulates at the macrophage-T cell interface and promotes cell clustering. SIGLEC1 ligands were enriched on activated T cells, and their blockade reduced cytotoxic cytokine production ex vivo. Single-cell (spatial) transcriptomics across independent ICB-treated melanoma cohorts showed that SIGLEC1 macrophages localize near CD8 T cells and are enriched in responders, where they also associate with T cells expressing activation/exhaustion markers. These findings define a SIGLEC1-dependent macrophage-T cell niche linked to effective immunotherapy.

immunology↗

bioRxiv2026

CMAS dampens anti-tumor immunity and associates with response to neoadjuvant immunotherapy in melanoma

Identifying immune-regulatory pathways to predict response is crucial for the efficacy of immune checkpoint blockade (ICB) immunotherapies. Sialylation is upregulated in tumor cells and modulates immune responses in cancer, yet its impact on patient clinical outcome and the spatial organization of the tumor microenvironment remain unclear. Here, using publicly available single-cell RNA sequencing data we show that expression of the sialylation master regulator CMAS in melanoma cells correlates with poorer patient survival. Using a murine melanoma model, we demonstrate that Cmas deletion in tumor cells severely impaired tumor growth and improved anti-tumor lymphoid and myeloid cell responses, increasing tumor cell-intrinsic susceptibility to interferon-gamma-, CD4+ T cell-, and macrophage-mediated killing. Single-cell spatial transcriptomics on neoadjuvant ICB-treated melanoma patient tumor biopsies revealed that CMAS expression in tumor cells inversely correlated with tumor cell proximity to and activation status of T cells and macrophages. Furthermore, expression of CMAS in tumor cells was increased in patients who did not respond to immunotherapy, compared to responders. Overall, our work identifies CMAS as a key modulator of tumor-immune dynamics associated with survival and response to neoadjuvant ICB immunotherapy in melanoma patients.

cancer biology↗

bioRxiv2022

Genomic and transcriptomic landscape of advanced renal cell cancer to individualize treatment strategy

BackgroundDifferences in the clinical course and treatment responses in individual patients with advanced renal cell carcinoma (RCC) can largely be explained by the different genomics of this disease. To improve the personalized treatment strategy and survival outcomes for patients with advanced RCC, the genomic make-up in patients with advanced RCC was investigated to identify putative actionable mutations and signatures. MethodsIn this prospective multicenter study (NCT01855477), whole-genome sequencing (WGS) data of locally advanced and metastatic tissue biopsies and matched whole-blood samples were collected from 91 patients with histopathologically confirmed RCC. WGS data were analyzed for small somatic variants, copy-number alterations and structural variants. For a subgroup of patients, RNA sequencing (RNA-Seq) data could be analyzed. RNA-Seq data were clustered on immunogenic and angiogenic gene expression patterns according to a previously developed angio-immunogenic gene signature. ResultsFor papillary and clear cell RCC, putative actionable drug targets were detected by WGS in 100% of the patients. RNA-Seq data of clear cell and papillary RCC were clustered using a previously developed angio-immunogenic gene signature. Analyses of driver mutations and RNA-Seq data revealed clear differences among different RCC subtypes, showing the added value of WGS and RNA-Seq over clinicopathological data. ConclusionsBy improving both histological subtyping and the selection of treatment according to actionable targets and immune signatures, WGS and RNA-Seq may improve therapeutic decision making for most patients with advanced RCC, including patients with non-clear cell RCC for whom no standard treatment is available to data. Prospective clinical trials are needed to evaluate the impact of genomic and transcriptomic diagnostics on survival outcome for advanced RCC patients.

cancer biology↗

bioRxiv2018

Pan-cancer whole genome analyses of metastatic solid tumors

Metastatic cancer is one of the major causes of death and is associated with poor treatment efficiency. A better understanding of the characteristics of late stage cancer is required to help tailor personalised treatment, reduce overtreatment and improve outcomes. Here we describe the largest pan-cancer study of metastatic solid tumor genomes, including 2,520 whole genome-sequenced tumor-normal pairs, analyzed at a median depth of 106x and 38x respectively, and surveying over 70 million somatic variants. Metastatic lesions were found to be very diverse, with mutation characteristics reflecting those of the primary tumor types, although with high rates of whole genome duplication events (56%). Metastatic lesions are relatively homogeneous with the vast majority (96%) of driver mutations being clonal and up to 80% of tumor suppressor genes bi-allelically inactivated through different mutational mechanisms. For 62% of all patients, genetic variants that may be associated with outcome of approved or experimental therapies were detected. These actionable events were distributed across various mutation types underlining the importance of comprehensive genomic tumor profiling for cancer precision medicine.

cancer biology↗

bioRxiv2018

INKA, an integrative data analysis pipeline for phosphoproteomic inference of active phosphokinases

Identifying (hyper)active kinases in cancer patient tumors is crucial to enable individualized treatment with specific inhibitors. Conceptually, kinase activity can be gleaned from global protein phosphorylation profiles obtained with mass spectrometry-based phosphoproteomics. A major challenge is to relate such profiles to specific kinases to identify (hyper)active kinases that may fuel growth/progression of individual tumors. Approaches have hitherto focused on phosphorylation of either kinases or their substrates. Here, we combine kinase-centric and substrate-centric information in an Integrative Inferred Kinase Activity (INKA) analysis. INKA utilizes label-free quantification of phosphopeptides derived from kinases, kinase activation loops, kinase substrates deduced from prior experimental knowledge, and kinase substrates predicted from sequence motifs, yielding a single score. This multipronged, stringent analysis enables ranking of kinase activity and visualization of kinase-substrate relation networks in a biological sample. As a proof of concept, INKA scoring of phosphoproteomic data for different oncogene-driven cancer cell lines inferred top activity of implicated driver kinases, and relevant quantitative changes upon perturbation. These analyses show the ability of INKA scoring to identify (hyper)active kinases, with potential clinical significance.

systems biology↗