Biology subjects

Feodoroff, M.

Publications and source records attributed to Feodoroff, M..

3 recordsLinked to original sources

bioRxiv2026

Site-Dependent Decoupling of Drug-Biomarker Associations in Clear Cell Renal Cell Carcinoma Revealed by Functional Profiling of Patient-Derived Cell Models

Clear cell renal cell carcinoma (ccRCC) frequently exhibits primary and acquired resistance to standard-of-care therapies, underscoring the need for improved strategies to predict therapeutic response and prioritize patient-specific treatments. Although recent multi-omic and single-cell studies have provided insight into the molecular landscape of ccRCC, molecular alterations alone incompletely predict drug sensitivity. We prospectively profiled tumors from 28 patients with localized and metastatic ccRCC by integrating molecular characterization, functional drug sensitivity and resistance testing in patient-derived cell models, and longitudinal clinical data. Although genomic biomarkers suggested potentially actionable therapies in 27/28 patients, functional testing revealed discordance between genomic actionability and ex vivo drug sensitivity, whereas linear mixed-effects modelling uncovered 16 novel copy-number-based features associated with sensitivity to 11 drugs. Genotype-drug response associations were largely preserved between primary tumors and vena cava thrombi but frequently disrupted in distant metastases. Integrating functional drug testing with multi-omic profiling reveals vulnerabilities not apparent from genomic data alone, refines therapeutic actionability, captures interpatient and intersite heterogeneity in ccRCC, and provides a scalable framework for individualized treatment prioritization.

cancer biology↗

bioRxiv2024

Achieving high-resolution single-cell segmentation in convoluted cancer spheroids via Bayesian optimization and deep-learning

Accurate quantification of drug responses in 3D tumor-immune co-cultures remains challenging because complex spatial architecture and cellular heterogeneity limit the interpretability of bulk viability assays. Here, we present MorphoNavigator-3D ('Morphological Navigator in 3D';MoNa-3D), an automated framework for high-resolution, annotation-free single-cell analysis in complex 3D co-cultures. The approach integrates optimized live-cell staining, deep learning-based segmentation, and Bayesian optimization (BO) to adapt end-to-end image-analysis workflows across diverse experimental conditions. MoNa-3D was applied to clear cell renal cell carcinoma (ccRCC)-immune cell 3D-spheroid co-cultures, exposed to PI3K/mTOR pathway inhibitors and immunomodulatory compounds in a high-content imaging-based drug screen. The pipeline was used to extract multiscale phenotypic features encompassing ATP-based cell viability, morphology, nuclear remodeling, spatial dispersion, and immune infiltration. This analysis resolved distinct drug-induced phenotypes: PI3K/mTOR inhibitors promoted spheroid disintegration, nuclear enlargement, and immune exclusion, whereas immunomodulators preserved spheroid architecture and T-cell engagement. Multivariate phenotypic integration distinguished drug classes and revealed intra-class variation, including divergent spatial responses to dual PI3K/mTOR versus mTORC1 inhibition. These phenotypes were consistent with known drug mechanisms, supporting the biological interpretability of the framework. Together, these findings establish MoNa-3D as a generalizable platform for multidimensional phenotypic profiling across complex 3D multicellular systems, supporting applications in drug discovery, tumor-immune interaction studies, and precision oncology.

cancer biology↗

bioRxiv2021

A novel immunopeptidomic-based pipeline for the generation of personalized oncolytic cancer vaccines

Beside the isolation and identification of MHC-I restricted peptides from the surface of cancer cells, one of the challenges is eliciting an effective anti-tumor CD8+ T cell mediated response as part of therapeutic cancer vaccine. Therefore, the establishment of a solid pipeline for the downstream selection of clinically relevant peptides and the subsequent creation of therapeutic cancer vaccines are of utmost importance. Indeed, the use of peptides for eliciting specific anti-tumor adaptive immunity is hindered by two main limitations: the efficient selection of the most optimal candidate peptides and the use of a highly immunogenic platform to combine with the peptides to induce effective tumor-specific adaptive immune responses. Here, we describe for the first time a streamlined pipeline for the generation of personalized cancer vaccines starting from the isolation and selection of the most immunogenic peptide candidates expressed on the tumor cells and ending in the generation of efficient therapeutic oncolytic cancer vaccines. This immunopeptidomics-based pipeline was carefully validated in a murine colon tumor model CT26. Specifically, we used state-of-the-art immunoprecipitation and mass spectrometric methodologies to isolate >8000 peptide targets from the CT26 tumor cell line. The selection of the target candidates was then based on two separate approaches: RNAseq analysis and the HEX software. The latter is a tool previously developed by Chiaro et al. (1), able to identify tumor antigens similar to pathogen antigens, in order to exploit molecular mimicry and tumor pathogen cross-reactive T-cells in cancer vaccine development. The generated list of candidates (twenty-six in total) was further tested in a functional characterization assay using interferon-{gamma} ELISpot (Enzyme-Linked Immunospot), reducing the number of candidates to six. These peptides were then tested in our previously described oncolytic cancer vaccine platform PeptiCRAd, a vaccine platform that combines an immunogenic oncolytic adenovirus (OAd) coated with tumor antigen peptides. In our work, PeptiCRAd was successfully used for the treatment of mice bearing CT26, controlling the primary malignant lesion and most importantly a secondary, non-treated, cancer lesion. These results confirmed the feasibility of applying the described pipeline for the selection of peptide candidates and generation of therapeutic oncolytic cancer vaccine, filling a gap in the field of cancer immunotherapy, and paving the way to translate our pipeline into human therapeutic approach.

cancer biology↗