Biology subjects

Noviello, T. M. R.

Publications and source records attributed to Noviello, T. M. R..

6 recordsLinked to original sources

bioRxiv2025

Transposable Elements and Homotypic Niches Drive Immune Dynamics and Resistance in Melanoma Epigenetic-based immunotherapy

Melanoma plasticity drives immune evasion and therapy resistance through dynamic cell-state transitions beyond genetic alterations. Epigenetic remodeling critically influences such processes, yet its role in reshaping the tumor ecosystem under therapeutic pressure remains unresolved. Here, we profiled longitudinal biopsies from melanoma patients treated in the phase Ib NIBIT-M4 epi-immunotherapy clinical trial (NCT02608437), testing the combination of a DNMT1 inhibitor with anti-CTLA4 using single-cell multiome and high-resolution spatial transcriptomics. Integrated analyses resolved seven malignant meta-programs, including a rare Wnt/{beta}-catenin-driven melanocytic state and a de-differentiated neural crest-like state enriched in non-responders. Spatial modeling revealed that homotypic clustering stabilizes resistant programs, with neural crest-like cells forming compact, centrally localized niches, whereas Wnt/{beta}-catenin subpopulations displayed a bimodal architecture, either cohesive clusters sustained by adhesion or dispersed, transcriptionally plastic cells. Responders exhibited progressive enrichment of an antigen presentation/interferon program and coordinated remodeling of the tumor microenvironment with T and B cell expansion, whereas tumors from non-responder patients maintained stable composition of neural crest-like clusters. Epigenetic therapy reactivated transposable elements, providing both regulatory signals that prime innate immunity within microenvironment and generating antigens that drive immunoediting and immunogenicity of Antigen presentation/interferon cell states in responders. Finally, NFATC2 emerged as a master regulator of neural crests-like transcriptional phenotypes and promoter of resistance to therapeutic interventions in melanoma patients. NFATC2 perturbation was able to shift tumor cells towards more differentiated and immunogenic states. These findings reveal how epigenetic-based immunotherapy reshapes melanoma ecosystems, provide mechanistic insights into how multiple transcriptional programs promote tumor plasticity and resistance to both combinatorial therapies and immune checkpoint blockade, identify spatial clustering as a principle stabilizing resistant niches, and highlight {beta}-catenin and NFATC2 as actionable vulnerabilities to overcome resistance.

genomics↗

bioRxiv2025

Integrated multi-omics profiling reveals the role of the DNA methylation landscape in shaping biological heterogeneity and clinical behaviour of metastatic melanoma

The biological and clinical relevance of the DNA methylation landscape in metastatic melanoma (MM) remains underexplored. In a retrospective cohort of 191 MM lesions from 165 AJCC Stage III and IV patients (EPICA cohort) we identified four tumor subsets (i.e. DEMethylated, LOW, INTermediate and CIMP) with progressively increasing levels of DNA methylation. These findings were validated in the TCGA MM. In EPICA, patients with LOW methylation tumors exhibited a significantly longer survival and a lower progression rate to more advanced AJCC stages, compared to patients with CIMP tumors. Furthermore, in an independent adjuvant immune checkpoint blockade MM cohort, patients with DEM/LOW pre-therapy lesions showed significantly longer relapse-free survival compared to those with INT/CIMP lesions. RNA-seq data analysis revealed that LOW and CIMP EPICA tumors showed opposite activation of master molecules influencing prognostic target genes, and differential expression of immunotherapy response and melanoma differentiation signatures. Compared to CIMP tumors, LOW lesions showed enrichment for pre-exhausted and exhausted T cells and more frequent retention of HLA Class I antigens. The differentiation and immune-related transcriptional features associated with LOW vs CIMP lesions were tumor-intrinsic programs retained in-vitro by melanoma cell lines. Consistently, treatment of differentiated melanoma cell lines with a DNMT inhibitor induced global DNA de-methylation, promoted de-differentiation and upregulated viral mimicry and IFNG predictive signatures of immunotherapy response. These findings underscore the role of DNA methylation in driving MM biological and clinical heterogeneity and support exploration of methylome targeting strategies for precision immunotherapy in melanoma.

cancer biology↗

bioRxiv2024

Targeting ZNF638 activates antiviral immune responses and potentiates immune checkpoint inhibition in glioblastoma

Viral mimicry refers to the activation of innate anti-viral immune responses due to the induction of endogenous retroelement (RE) expression. Viral mimicry has been previously described to augment anti-tumor immune responses and sensitize solid tumors to immunotherapy including colorectal cancer, melanoma, and clear renal cell carcinoma. Here, we found that targeting a novel, master epigenetic regulator, Zinc Finger Protein 638 (ZNF638), induces viral mimicry in glioblastoma (GBM) preclinical models and potentiates immune checkpoint inhibition (ICI). ZNF638 recruits the HUSH complex, which precipitates repressive H3K9me3 marks on endogenous REs. In GBM, ZNF638 is associated with marked locoregional immunosuppressive transcriptional signatures, reduced endogenous RE expression and poor immune cell infiltration (CD8+ T-cells, dendritic cells). ZNF638 knockdown decreased H3K9-trimethylation, increased cytosolic dsRNA and activated intracellular dsRNA-signaling cascades (RIG-I, MDA5 and IRF3). Furthermore, ZNF638 knockdown upregulated antiviral immune programs and significantly increased PD-L1 immune checkpoint expression in patient-derived GBM neurospheres and diverse murine models. Importantly, targeting ZNF638 sensitized mice to ICI in syngeneic murine orthotopic models through innate interferon signaling. This response was recapitulated in recurrent GBM (rGBM) samples with radiographic responses to checkpoint inhibition with widely increased expression of dsRNA, PD-L1 and perivascular CD8 cell infiltration, suggesting dsRNA-signaling may mediate response to immunotherapy. Finally, we showed that low ZNF638 expression was a biomarker of clinical response to ICI and improved survival in rGBM patients and melanoma patients. Our findings suggest that ZNF638 could serve as a target to potentiate immunotherapy in gliomas.

cancer biology↗

bioRxiv2024

DNA methylation status classifies pleural mesothelioma cells according to their immune profile: implication for precision epigenetic therapy

Backgroundco-targeting of immune checkpoint inhibitors (ICI) CTLA-4 and PD-1 has recently become the new first-line standard of care therapy of pleural mesothelioma (PM) patients, with a significant improvement of overall survival over conventional chemotherapy. The analysis by tumor histotype demonstrated a greater efficacy of ICI therapy in non-epithelioid (non-E) vs epithelioid (E) PM; although some E PM patients also benefit from treatment. This evidence suggests that molecular tumor features, beyond histotype, could be relevant to improve the efficacy of ICI therapy in PM. Among these, tumor DNA methylation emerges as a promising factor to explore, due to its potential role in driving the immune phenotype of cancer cells. Thus, we utilized a panel of cultured PM cells of different histotype, to provide preclinical evidence supporting the role of the tumor methylation landscape and of its pharmacologic modulation, to prospectively improve the efficacy of ICI therapy of PM patients. Methodsthe methylome profile (EPIC array) of distinct E (#5) and non-E (#9) PM cell lines was analyzed, followed by integrated analysis with their associated transcriptomic profile (Clariom S array), before and after in vitro treatment with the DNA hypomethylating agent (DHA) guadecitabine. The most variable methylated probes were selected to calculate the methylation score (CIMP index) for each cell line at baseline. Genes that were differentially expressed and methylated were then selected for gene ontology analysis. Resultsthe CIMP index stratified PM cell lines in two distinct classes, CIMP (hyper-methylated; #7) and LOW (hypo-methylated; #7), regardless of their E or non-E histotype. Integrated analyses of methylome and transcriptome data revealed that CIMP PM cells had a substantial number of hyper-methylated, silenced genes, which negatively impacted their immune phenotype compared to LOW PM cells. Treatment with DHA reverted the methylation-driven immune-compromised profile of CIMP PM cells and enhanced the constitutive immune-favorable profile of LOW PM cells. Conclusionthe study highlighted the relevance of DNA methylation in shaping the constitutive immune classification of PM cells, that is independent from their histological subtypes. The identified role of DHA in shifting the phenotype of PM cells towards an immune-favorable state supports its role in clinical trials of precision epigenetic therapy combined with ICI.

cancer biology↗

bioRxiv2023

Robust Feature Selection strategy detects a panel of microRNAs as putative diagnostic biomarkers in Breast Cancer

MicroRNAs represent a comprehensive class of short, single-stranded, non-coding RNA transcripts able to interfere with the translation of their targets or degrade them. Since these molecules are dysregulated in several malignancies, they represent reliable biomarkers in various contexts. In this study, the application of several Feature Selection methods uncovers a panel of 20 microRNAs, of which hsa-mir-337, hsa-mir-378c, and hsa-mir-483 are still poorly investigated in the context of Breast Cancer. This signature is capable of discriminating between healthy and tumoral samples, showing better classification performance when compared with differentially expressed microRNAs. Furthermore, a network-based centrality analysis on the gene targets of these transcripts highlighted CDC25, TPX2, KIF18B, CDCA3, TGFBR2, CAV1, TNS1, and LHFPL6 as key dysregulated genes. This study provides in silico hypotheses as new insights for future in vivo or in vitro studies uncovering the role of these putative diagnostic biomarkers in Breast Cancer.

bioinformatics↗

bioRxiv2020

Deep learning approaches predict non-coding RNA functions from only raw sequence data

Non-coding RNAs (ncRNAs) are small non-coding sequences involved in gene regulation in many biological processes and diseases. The lack of a complete comprehension of their biological functionality, especially in a genome-wide scenario, has demanded new computational approaches to annotate their roles. It is widely known that secondary structure is determinant to know RNA function and machine learning based approaches have been successfully proven to predict RNA function from secondary structure information. Here we show that RNA function can be predicted with good accuracy from raw sequence information without the necessity of computing secondary structure features which is computationally expensive. This finding appears to go against the dogma of secondary structure being a key determinant of function in RNA. Compared to recent secondary structure based methods, the proposed solution is more robust to sequence boundary noise and reduces drastically the computational cost allowing for large data volume annotations. Scripts and datasets to reproduce the results of experiments proposed in this study are available at: https://github.com/bioinformatics-sannio/ncrna-deep

bioinformatics↗