Biology subjects

Falco, M. M.

Publications and source records attributed to Falco, M. M..

9 recordsLinked to original sources

bioRxiv2026

Principles of subclonal gene dosage across human cancer

Intratumor heterogeneity drives disease progression, but even though subclonal copy number variation (CNV) is a major contributor to this heterogeneity1, its impact on cell phenotype is not fully understood. Here, by applying high-quality joint whole genome sequencing and mRNA profiling in single cells (DNTR-seq2) to solid tumors and leukemias from 57 patients, we have analyzed the in vivo transcriptional effect of subclonal CNVs within a tumor. We found that gene dosage is generally additive in low and moderate copy states, but that cancer-type-specific compensation is common, and core promoter elements are associated with reduced additivity. We find that different classes of subclonal CNV impose varying degrees of transcriptional constraints on a cell, with highly amplified megabase-size regions associated with a strong effect both in cis and trans, while arm level CNVs generally exert a milder effect. We also describe a previously unappreciated class of tumors with transient clonality, where every cell is genetically highly distinct. We find that transient clonality is common in ovarian cancer and soft tissue sarcoma, that it is preceded by a whole genome duplication event, and that gene dosage in these tumors affects transcript abundance to a similar degree as in cancer with stable subclones.

cancer biology↗

bioRxiv2024

Single-cell spatial atlas of high-grade serous ovarian cancer unveils MHC class II as a key driver of spatial tumor ecosystems and clinical outcomes

The tumor microenvironment (TME) is a complex network of interactions between malignant and host cells, yet its orchestration in advanced high-grade serous ovarian carcinoma (HGSC) remains poorly understood. We present a comprehensive single-cell spatial atlas of 280 metastatic HGSCs, integrating high-dimensional imaging, genomics, and transcriptomics. Using 929 single-cell maps, we identify distinct spatial domains associated with phenotypically heterogeneous cellular compositions, and demonstrate that immune cell co-infiltration at the tumor-stroma interface significantly influences clinical outcomes. To uncover the key drivers of the tumor ecosystem, we developed CEFIIRA (Cell Feature Importance Identification by RAndom forest), which identified tumor cell-intrinsic MHC-II expression as a critical predictor of prolonged survival, independent of clinicomolecular profiles. Validation with external datasets confirmed that MHC-II-expressing cancer cells drive immune infiltration and orchestrate spatial tumor-immune interactions. Our atlas offers novel insights into immune surveillance mechanisms across HGSC clinicomolecular groups, paving the way for improved therapeutic strategies and patient stratification.

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

Chemotherapy induces myeloid-driven spatial T-cell exhaustion in ovarian cancer

To uncover the intricate, chemotherapy-induced spatiotemporal remodeling of the tumor microenvironment, we conducted integrative spatial and molecular characterization of 97 high-grade serous ovarian cancer (HGSC) samples collected before and after chemotherapy. Using single-cell and spatial analyses, we identify increasingly versatile immune cell states, which form spatiotemporally dynamic microcommunities at the tumor-stroma interface. We demonstrate that chemotherapy triggers spatial redistribution and exhaustion of CD8+ T cells due to prolonged antigen presentation by macrophages, both within interconnected myeloid networks termed "Myelonets" and at the tumor stroma interface. Single-cell and spatial transcriptomics identifies prominent TIGIT-NECTIN2 ligand-receptor interactions induced by chemotherapy. Using a functional patient-derived immuno-oncology platform, we show that CD8+T-cell activity can be boosted by combining immune checkpoint blockade with chemotherapy. Our discovery of chemotherapy-induced myeloid-driven spatial T-cell exhaustion paves the way for novel immunotherapeutic strategies to unleash CD8+ T-cell-mediated anti-tumor immunity in HGSC.

cancer biology↗

bioRxiv2022

Tracing back primed resistance in cancer via sister cells

Exploring non-genetic evolution of cell states during cancer treatments has become attainable by recent advances in lineage-tracing methods. However, transcriptional changes that drive cells into resistant fates may be subtle, necessitating high resolution analysis. We developed ReSisTrace that uses shared transcriptomic features of synchronised sister cells to predict the states that prime treatment resistance. We applied ReSisTrace in ovarian cancer cells perturbed with olaparib, carboplatin or natural killer (NK) cells. The pre-resistant phenotypes were defined by cell cycle and proteostatic features, reflecting the traits enriched in the upcoming subclonal selection. Furthermore, DNA repair deficiency rendered cells susceptible to both DNA damaging agents and NK killing in a context-dependent manner. Finally, we leveraged the pre-resistance profiles to predict and validate small molecules driving cells to sensitive states prior to treatment. In summary, ReSisTrace resolves pre-existing transcriptional features of treatment vulnerability, facilitating both molecular patient stratification and discovery of synergistic pre-sensitizing therapies.

cancer biology↗

bioRxiv2022

A platform for efficient establishment, expansion and drug response profiling of high-grade serous ovarian cancer organoids

The broad research use of organoids from high-grade serous ovarian carcinoma (HGSC) has been hampered by low culture success rates and limited availability of fresh tumor material. Here we describe a method for generation and long-term expansion of HGSC organoids with efficacy markedly improved over previous reports (55% vs. 23-38%). We established organoids from cryopreserved material, demonstrating the feasibility of using viably biobanked tissue for HGSC organoid derivation. Genomic, histologic and single-cell transcriptomic analyses revealed that organoids recapitulated genetic and phenotypic features of original tumors. Organoid drug responses correlated with clinical treatment outcomes, although in culture conditions-dependent manner and only in organoids maintained in human plasma-like medium (HPLM). Organoids from consenting patients are available to the research community through a public biobank and organoid genomic data explorable through an interactive online tool. Taken together, this resource facilitates the application of HGSC organoids in basic and translational ovarian cancer research.

cancer biology↗

bioRxiv2021

miQC: An adaptive probabilistic framework for quality control of single-cell RNA-sequencing data

MotivationSingle-cell RNA-sequencing (scRNA-seq) has made it possible to profile gene expression in tissues at high resolution. An important preprocessing step prior to performing downstream analyses is to identify and remove cells with poor or degraded sample quality using quality control (QC) metrics. Two widely used QC metrics to identify a low-quality cell are (i) if the cell includes a high proportion of reads that map to mitochondrial DNA (mtDNA) encoded genes and (ii) if a small number of genes are detected. Current best practices use these QC metrics independently with either arbitrary, uniform thresholds (e.g. 5%) or biological context-dependent (e.g. species) thresholds, and fail to jointly model these metrics in a data-driven manner. Current practices are often overly stringent and especially untenable on lower-quality tissues, such as archived tumor tissues. ResultsWe propose a data-driven QC metric (miQC) that jointly models both the proportion of reads mapping to mtDNA genes and the number of detected genes with mixture models in a probabilistic framework to predict the low-quality cells in a given dataset. We demonstrate how our QC metric easily adapts to different types of single-cell datasets to remove low-quality cells while preserving high-quality cells that can be used for downstream analyses. AvailabilitySoftware available at https://github.com/greenelab/miQC. The code used to download datasets, perform the analyses, and reproduce the figures is available at https://github.com/greenelab/mito-filtering. ContactStephanie C. Hicks (shicks19@jhu.edu) and Anna Vaharautio (anna.vaharautio@helsinki.fi)

genomics↗

bioRxiv2020

Mechanistic modeling of the SARS-CoV-2 disease map

Here we present a web interface that implements a comprehensive mechanistic model of the SARS-CoV-2 disease map in which the detailed activity of the human signaling circuits related to the viral infection and the different antiviral responses, including immune and inflammatory activities, can be inferred from gene expression experiments. Moreover, given to the mechanistic properties of the model, the effect of potential interventions, such as knock-downs, over-expression or drug effects (currently the system models the effect of more than 8000 DrugBank drugs) can be studied in specific conditions. By providing a holistic, systems biology approach to the understanding of the complexities of the viral infection process, this tool will become an important asset in the search for efficient antiviral treatments. The tool is freely available at: http://hipathia.babelomics.org/covid19/

systems biology↗

bioRxiv2019

Mechanistic models of signaling pathways deconvolute the functional landscape of glioblastoma at single cell resolution

The rapid development of single cell RNA-sequencing (scRNA-seq) technologies is revealing an unexpectedly large degree of heterogeneity in gene expression levels across the different cells that compose the same tissue sample. However, little is known on the functional consequences of this heterogeneity and the contribution of individual cell-fate decisions to the collective behavior of the tissues these cells are part of. Mechanistic models of signaling pathways have already proven to be useful tools for understanding relevant aspects of cell functionality. Here we propose to use this mechanistic modeling strategy to deconvolute the complexity of the functional behavior of a tissue by dissecting it into the individual functional landscapes of its component cells by using a single-cell RNA-seq experiment of glioblastoma cells. This mechanistic modeling analysis revealed a high degree of heterogeneity at the scale of signaling circuits, suggesting the existence of a complex functional landscape at single cell level. Different clusters of neoplastic glioblastoma cells have been characterized according to their differences in signaling circuit activity profiles, which only partly overlap with the conventional glioblastoma subtype classification. The activity of signaling circuits that trigger cell functionalities which can easily be assimilated to cancer hallmarks reveals different functional strategies with different degrees of aggressiveness followed by any of the clusters. In addition, mechanistic modeling allows simulating the effect of interventions on the components of the signaling circuits, such as drug inhibitions. Thus, effects of drug inhibitions at single cell level can be dissected, revealing for the first time the mechanisms that individual cells use to avoid the effect of a targeted therapy which explain why and how a small proportion of cells display, in fact, different degrees of resistance to the treatment. The results presented here strongly suggest that mechanistic modeling at single cell level not only allows uncovering the molecular mechanisms of the tumor progression but also can predict the success of a treatment and can contribute to a better definition of therapeutic targets in the future.

systems biology↗