Publications by authors named "Julia Casado"

Homologous recombination DNA-repair deficiency (HRD) is a common driver of genomic instability and confers a therapeutic vulnerability in cancer. The accurate detection of somatic allelic imbalances (AIs) has been limited by methods focused on BRCA1/2 mutations and using mixtures of cancer types. Using pan-cancer data, we revealed distinct patterns of AIs in high-grade serous ovarian cancer (HGSC).

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Unlabelled: Lysophosphatidic acid (LPA) is a bioactive lipid enriched in the tumor microenvironment of immunosuppressive malignancies such as ovarian cancer. Although LPA enhances the tumorigenic attributes of cancer cells, the immunomodulatory activity of this phospholipid messenger remains largely unexplored. Here, we report that LPA operates as a negative regulator of type I interferon (IFN) responses in ovarian cancer.

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The mechanisms regulating exhaustion of tumor-infiltrating lymphocytes (TIL) and responsiveness to PD-1 blockade remain partly unknown. In human ovarian cancer, we show that tumor-specific CD8 TIL accumulate in tumor islets, where they engage antigen and upregulate PD-1, which restrains their functions. Intraepithelial PD-1CD8 TIL can be, however, polyfunctional.

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Although shunt overdrainage is a well-known complication in hydrocephalus management, the problem has been underestimated. Current literature suggests that the topic requires more examination. An insight into this condition is limited by a lack of universally agreed-upon diagnostic criteria, heterogeneity of published series, the multitude of different management options and misunderstanding of relationships among pathophysiological mechanisms involved.

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Despite advances in immuno-oncology, the relationship between tumor genotypes and response to immunotherapy remains poorly understood, particularly in high-grade serous tubo-ovarian carcinomas (HGSC). We developed a series of mouse models that carry genotypes of human HGSCs and grow in syngeneic immunocompetent hosts to address this gap. We transformed murine-fallopian tube epithelial cells to phenocopy homologous recombination-deficient tumors through a combined loss of and overexpression of and , which was contrasted with an identical model carrying wild-type .

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Motivation: Single-cell proteomics technologies, such as mass cytometry, have enabled characterization of cell-to-cell variation and cell populations at a single-cell resolution. These large amounts of data, require dedicated, interactive tools for translating the data into knowledge.

Results: We present a comprehensive, interactive method called Cyto to streamline analysis of large-scale cytometry data.

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Motivation: Non-parametric dimensionality reduction techniques, such as t-distributed stochastic neighbor embedding (t-SNE), are the most frequently used methods in the exploratory analysis of single-cell datasets. Current implementations scale poorly to massive datasets and often require downsampling or interpolative approximations, which can leave less-frequent populations undiscovered and much information unexploited.

Results: We implemented a fast t-SNE package, qSNE, which uses a quasi-Newton optimizer, allowing quadratic convergence rate and automatic perplexity (level of detail) optimizer.

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Combined PARP and immune checkpoint inhibition has yielded encouraging results in ovarian cancer, but predictive biomarkers are lacking. We performed immunogenomic profiling and highly multiplexed single-cell imaging on tumor samples from patients enrolled in a Phase I/II trial of niraparib and pembrolizumab in ovarian cancer (NCT02657889). We identify two determinants of response; mutational signature 3 reflecting defective homologous recombination DNA repair, and positive immune score as a surrogate of interferon-primed exhausted CD8 + T-cells in the tumor microenvironment.

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Background: The epigenome plays a key role in cancer heterogeneity and drug resistance. Hence, a number of epigenetic inhibitors have been developed and tested in cancers. The major focus of most studies so far has been on the cytotoxic effect of these compounds, and only few have investigated the ability to revert the resistant phenotype in cancer cells.

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Summary: Anduril is an analysis and integration framework that facilitates the design, use, parallelization and reproducibility of bioinformatics workflows. Anduril has been upgraded to use Scala for pipeline construction, which simplifies software maintenance, and facilitates design of complex pipelines. Additionally, Anduril's bioinformatics repository has been expanded with multiple components, and tutorial pipelines, for next-generation sequencing data analysis.

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