Biology subjects

Marchi, G.

Publications and source records attributed to Marchi, G..

6 recordsLinked to original sources

bioRxiv2025

Multi-modal characterization of transcriptional programs that drive metastatic cascades to solid sites and ascites in ovarian cancer

Ovarian high-grade serous carcinoma (HGSC) is characterized by extensive intra-peritoneal dissemination and tumor heterogeneity. In the metastatic cascade, tumors utilize several transcriptional programs to translocate and survive in distant tissues. Here, we analyzed multi-modal, real-world data from 350 tumor samples across 160 patients with HGSC to identify transcriptional programs that drive intra-peritoneal metastasis and heterogeneity. We identified nine transcriptional programs, including those regulating epithelial-mesenchymal transition and immune modulation and cytoskeletal reorganization, which shape distinct metastatic trajectories to solid and ascitic environments and are associated to treatment response. Our results reveal pronounced intra-patient transcriptional heterogeneity, which in some cases surpassed inter-patient heterogeneity, highlighting the importance of multi-site sampling for accurate prognostication and combinatorial treatments in HGSC. Our extensive characterization offers novel insights into intra-peritoneal metastasis with significant prognostic implications, reveals histomorphological biomarkers for patient stratification and paves the way for innovative therapeutic strategies aimed at impairing cancer cell adaptability and limiting metastasis.

cancer biology↗

bioRxiv2025

Multi-Omics Characterization of ctDNA Release Mechanisms in Ovarian Cancer

Circulating tumor DNA (ctDNA) is a powerful biomarker capable to predict tumor dynamics and treatment response. Despite its importance, the biological mechanisms behind ctDNA release have remained unclear. The variability among patients is affected by cancer burden, histology and stage, but these factors cover only a part of the detected variability. Herein, we characterized molecular drivers of baseline ctDNA variability in a real-world cohort of 118 patients with ovarian high-grade serous carcinoma (HGSC). Genomic and transcriptomic analyses revealed a strong positive correlation between ctDNA levels and cellular proliferation, and an inverse relationship with immune activity. Particularly, low ctDNA tumors exhibited higher bulk expression of mucins and CIITA, suggesting that intrinsic immune responses and immune landscape are linked to ctDNA release. The low and high ctDNA levels were significantly associated with poor prognosis compared to medium level in unresectable HGSC patients treated with neoadjuvant chemotherapy. In summary, our results suggest that ctDNA release is modulated by cancer cell proliferation and tumor microenvironment.

cancer biology↗

bioRxiv2024

Multi-Omics Analysis Reveals the Attenuation of the Interferon Pathway as a Driver of Chemo-Refractory Ovarian Cancer

Ovarian high-grade serous carcinoma (HGSC) represents the deadliest gynecological malignancy, with 10-15% of patients exhibiting primary resistance to first-line chemotherapy. These primarily chemo-refractory patients have particularly poor survival outcomes, emphasizing the urgent need for developing predictive biomarkers and novel therapeutic approaches. Here, we show that interferon type I (IFN-I) pathway activity in cancer cells is a crucial determinant of chemotherapy response in HGSC. Through a comprehensive multi-omics analysis within the DECIDER observational trial (ClinicalTrials.gov identifier NCT04846933) cohort, we identified that chemo-refractory HGSC is characterized by diminished IFN-I and enhanced hypoxia pathway activities. Importantly, IFN-I pathway activity was independently prognostic for patient survival, highlighting its potential as a biomarker. Our results elucidate the heterogeneity of treatment response at the molecular level and suggest that augmentation of IFN-I response could enhance chemosensitivity in refractory cases. This study underscores the potential of the IFN-I pathway as a therapeutic target and advocates for the initiation of clinical trials testing external modulators of the IFN-I response, promising a significant stride forward in the treatment of refractory HGSC.

cancer biology↗

bioRxiv2024

Patient-derived functional immuno-oncology platform identifies responders to ATR inhibitor and immunotherapy combinations in ovarian cancer

Responses to single agent immunotherapies have remained modest in high-grade serous ovarian cancer (HGSC), suggesting the need for combination treatments. Identifying clinically effective immunotherapy combinations (IC) requires pre-clinical testing using models representing the patient-specific immune microenvironment. Here, we established a functional immuno-oncology platform for high-throughput and functional testing of IC using HGSC patient-derived immunocompetent cultures (iPDCs) established on patient-derived omentum gel matrix. We employed genomic and single-cell analysis to assess the intricate and functional characteristics of the iPDCs combined with tumor and immune cell-specific cytotoxic responses. Corroborating the clinical response to Poly (ADP-ribose) polymerase inhibitors (PARPi), iPDCs showed homologous recombination deficiency (HRD) - specific response to PARPi. Importantly, drug responses from iPDCs of chemotherapy and PARPi refractory patients corresponded with patient outcomes and aligned with distinct pathway activities from single-cell RNA sequencing analysis. Furthermore, iPDCs from HRD tumors showed response to anti-PD1 antibody as measured by decrease in tumor cells combined with augmented T cell activation. High-throughput drug testing followed by single cell-imaging from iPDCs revealed patient-specific responses to combination of ataxia telangiectasia and Rad3-related inhibitor (ATRi) with DNA damaging agents or immunotherapies. Integration of cytotoxic responses with immune cell states uncovered patient-specific immune activation with the combination of ATRi and a novel immunotherapy targeting Autotaxin (ATX), and this response was significantly associated with a tumor-cell replication stress biomarker in single-cell analysis of tCycIF highly multiplexed imaging. In conclusion, iPDCs provide a platform for high-throughput screening and functional testing of immuno-oncology agents for precision oncology in HGSC.

cancer biology↗

bioRxiv2023

Deciphering Cancer Genomes with GenomeSpy: A Grammar-Based Visualization Toolkit

BackgroundVisualization is an indispensable facet of genomic data analysis. Despite the abundance of specialized visualization tools, there remains a distinct need for tailored solutions. However, their implementation typically requires extensive programming expertise from bioinformaticians and software developers, especially when building interactive applications. Toolkits based on visualization grammars offer a more accessible, declarative way to author new visualizations. Nevertheless, current grammar-based solutions fall short in adequately supporting the interactive analysis of large data sets with extensive sample collections, a pivotal task often encountered in cancer research. ResultsWe present GenomeSpy, a grammar-based toolkit for authoring tailored, interactive visualizations for genomic data analysis. Users can implement new visualization designs with little effort by using combinatorial building blocks that are put together with a declarative language. These fully customizable visualizations can be embedded in web pages or end-user-oriented applications. The toolkit also includes a fully customizable but user-friendly application for analyzing sample collections, which may comprise genomic and clinical data. Findings can be bookmarked and shared as links that incorporate provenance information. A distinctive element of GenomeSpys architecture is its effective use of the graphics processing unit (GPU) in all rendering. GPU usage enables a high frame rate and smoothly animated interactions, such as navigation within a genome. We demonstrate the utility of GenomeSpy by characterizing the genomic landscape of 753 ovarian cancer samples from patients in the DECIDER clinical trial. Our results expand the understanding of the genomic architecture in ovarian cancer, particularly the diversity of chromosomal instability. We also show how GenomeSpy enabled the discovery of clinically actionable genomic aberrations. ConclusionsGenomeSpy is a visualization toolkit applicable to a wide range of tasks pertinent to genome analysis. It offers high flexibility and exceptional performance in interactive analysis. The toolkit is open source with an MIT license, implemented in JavaScript, and available at https://genomespy.app/.

bioinformatics↗

bioRxiv2021

Metagenomic sequencing for rapid identification of Xylella fastidiosa from leaf samples

Xylella fastidiosa (Xf) is a globally distributed plant pathogenic bacterium. The primary control strategy for Xf diseases is eradicating infected plants; therefore, timely and accurate detection is necessary to prevent crop losses and further pathogen dispersal. Conventional Xf diagnostics primarily relies on quantitative PCR (qPCR) assays. However, these methods do not consider new or emerging variants due to pathogen genetic recombination and sensitivity limitations. We developed and tested a metagenomics pipeline using in-house short-read sequencing as a complementary approach for affordable, fast, and highly accurate Xf detection. We used metagenomics to identify Xf to strain level in single and mixed infected plant samples at concentrations as low as one picogram of bacterial DNA per gram of tissue. We also tested naturally infected samples from various plant species originating from Europe and the United States. We identified Xf subspecies in samples previously considered inconclusive with real-time PCR (Cq > 35). Overall, we showed the versatility of the pipeline by using different plant hosts and DNA extraction methods. Our pipeline provides taxonomic and functional information for Xf diagnostics without extensive knowledge of the disease. We hope this pipeline can be used for early detection of Xf and incorporated as a tool to inform disease management strategies. IMPORTANCEXylella fastidiosa (Xf) destructive outbreaks in Europe highlight this pathogens capacity to expand its host range and geographical distribution. The current disease diagnostic approaches are limited by a multiple-step process, biases to known sequences, and detection limits. We developed a low-cost, user-friendly metagenomic sequencing tool for Xf detection. In less than three days, we were able to identify Xf subspecies and strains in field-collected samples. Overall, our pipeline is a diagnostics tool that could be easily extended to other plant-pathogen interactions and implemented for emerging plant threat surveillance.

microbiology↗