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Boniolo, F.

Publications and source records attributed to Boniolo, F..

3 recordsLinked to original sources

Distinct colorectal cancer genotypes shape microbial ecosystems and reveal stage-specific microbiota dependencies

The gut microbiota has emerged as an important modifier of colorectal cancer (CRC), yet how tumor genotype influences host-microbiota interactions and whether microbial signals are required throughout tumor progression remain unclear. Here, we combined genetically engineered mouse models, microbial multi-omics and a germ-free-compatible orthotopic transplantation system to define the functional contribution of the microbiota across distinct stages of CRC evolution. Across multiple CRC genotypes, we identified tumor-associated microbial ecosystem states characterized by distinct taxonomic, functional and metabolic configurations. To directly test their contribution to tumor progression, we established the first orthotopic CRC transplantation platform compatible with long-term experimentation in germ-free mice, enabling side-by-side comparison of genetically identical tumors in the presence or absence of microbiota. Using organoids spanning low-grade adenoma, high-grade adenoma and adenocarcinoma states, we found that the dependence on the presence of microbiota progressively decreases during malignant evolution. Whereas adenoma-derived organoids exhibited profound dependence on microbial exposure and failed or were markedly impaired in establishing tumors under germ-free conditions, adenocarcinoma organoids engrafted and metastasized in both germ-free and specific pathogen-free (SPF) hosts. Unexpectedly, comprehensive histological, immunological and transcriptomic analyses revealed highly similar tumor ecosystem states in advanced tumors arising under both microbial conditions, arguing against broad immune or epithelial defects as a primary explanation for the observed phenotype. Together, our findings demonstrate that distinct oncogenic drivers establish specific microbial ecosystem states and reveal a stage-dependent role of the microbiota during colorectal tumorigenesis. Whereas microbial signals are critical during early stages of tumor progression and may promote malignant transformation, advanced tumors progressively acquire microbiota-independent growth programs and increasingly impose genotype-specific ecological signatures on the surrounding microbial ecosystem. More broadly, we establish a versatile framework for the causal dissection of tumor-microbiota interactions in cancer.

cancer biology↗

Chromatin Perturbation Promotes Susceptibility to Hypomethylating Agents

Cancer-directed drugs are often clinically deployed without definitive understanding of their molecular mechanisms of action (MOA). Hypomethylating agents (HMAs), which result in the degradation of the DNA methyltransferase 1 (DNMT1), have been deployed for decades in the treatment of haematological malignancies1,2. The precise mechanism of action of these drugs, however, has been debated, rendering the design of rational combination therapies challenging. Here, we identified the deubiquitinating enzyme USP48 as a crucial regulator of posttranslational histone modification in the context of DNA demethylation. USP48 loss selectively enhances response to DNMT1 inhibition, leading to a rapid induction of cell death. We demonstrate that USP48 is localized at sites of DNA damage and deubiquitinates H2A variants and proteins important for DNA damage repair. Functionally, loss of USP48 triggers an increase in chromatin accessibility upon HMA treatment, rendering AML cells more susceptible to DNA damage. Our results support USP48 as a posttranslational histone modifier for chromatin stability and DNA damage in response to HMA-related DNA demethylation. These findings propose USP48 as a new target for combination therapy with HMAs for acute myeloid leukaemia (AML).

cancer biology↗

spongEffects: ceRNA modules offer patient-specific insights into the miRNA regulatory landscape

MotivationCancer is one of the leading causes of death worldwide. Despite significant improvements in prevention and treatment, mortality remains high for many cancer types. Hence, innovative methods that use molecular data to stratify patients and identify biomarkers are needed. Promising biomarkers can also be inferred from competing endogenous RNA (ceRNA) networks that capture the gene-miRNA gene regulatory landscape. Thus far, the role of these biomarkers could only be studied globally but not in a sample-specific manner. To mitigate this, we introduce spongEffects, a novel method that infers subnetworks (or modules) from ceRNA networks and calculates patient- or sample-specific scores related to their regulatory activity. ResultsWe show how spongEffects can be used for downstream interpretation and machine learning tasks such as tumor classification and for identifying subtype-specific regulatory interactions. In a concrete example of breast cancer subtype classification, we prioritize modules impacting the biology of the different subtypes. In summary, spongEffects prioritizes ceRNA modules as biomarkers and offers insights into the miRNA regulatory landscape. Notably, these module scores can be inferred from gene expression data alone and can thus be applied to cohorts where miRNA expression information is lacking. Availabilityhttps://bioconductor.org/packages/devel/bioc/html/SPONGE.html Contactmarkus.daniel.hoffmann@tum.de; markus.list@tum.de Supplementary informationSupplementary data are available at Bioinformatics online.

systems biology↗