Publications by authors named "Carlota Rubio-Perez"

The tumor immune microenvironment is a main contributor to cancer progression and a promising therapeutic target for oncology. However, immune microenvironments vary profoundly between patients, and biomarkers for prognosis and treatment response lack precision. A comprehensive compendium of tumor immune cells is required to pinpoint predictive cellular states and their spatial localization.

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Tyrosine kinase inhibitors were found to be clinically effective for treatment of patients with certain subsets of cancers carrying somatic mutations in receptor tyrosine kinases. However, the duration of clinical response is often limited, and patients ultimately develop drug resistance. Here, we use single-cell RNA sequencing to demonstrate the existence of multiple cancer cell subpopulations within cell lines, xenograft tumors and patient tumors.

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Brain metastases are the most common tumor of the brain with a dismal prognosis. A fraction of patients with brain metastasis benefit from treatment with immune checkpoint inhibitors (ICI) and the degree and phenotype of the immune cell infiltration has been used to predict response to ICI. However, the anatomical location of brain lesions limits access to tumor material to characterize the immune phenotype.

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Article Synopsis
  • - The study investigates how SARS-CoV-2 affects the placenta and pregnancy outcomes by analyzing tissues from both infected and non-infected pregnant women.
  • - SARS-CoV-2 was found in about half of the placental tissues from infected women, but generally did not correlate with major health issues for mothers or newborns, except in one case with severe damage.
  • - The research indicates that while the placenta can block vertical transmission of the virus, severe infections can still cause significant damage, potentially leading to problems for the newborn.
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Background Reliable predictive imaging markers of response to immune checkpoint inhibitors are needed. Purpose To develop and validate a pretreatment CT-based radiomics signature to predict response to immune checkpoint inhibitors in advanced solid tumors. Materials and Methods In this retrospective study, a radiomics signature was developed in patients with advanced solid tumors (including breast, cervix, gastrointestinal) treated with anti-programmed cell death-1 or programmed cell death ligand-1 monotherapy from August 2012 to May 2018 (cohort 1).

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The molecular characterisation of medulloblastoma, the most common paediatric brain tumour, is crucial for the correct management and treatment of this heterogenous disease. However, insufficient tissue sample, the presence of tumour heterogeneity, or disseminated disease can challenge its diagnosis and monitoring. Here, we report that the cerebrospinal fluid (CSF) circulating tumour DNA (ctDNA) recapitulates the genomic alterations of the tumour and facilitates subgrouping and risk stratification, providing valuable information about diagnosis and prognosis.

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DYRK (dual-specificity tyrosine-regulated kinases) are an evolutionary conserved family of protein kinases with members from yeast to humans. In humans, DYRKs are pleiotropic factors that phosphorylate a broad set of proteins involved in many different cellular processes. These include factors that have been associated with all the hallmarks of cancer, from genomic instability to increased proliferation and resistance, programmed cell death, or signaling pathways whose dysfunction is relevant to tumor onset and progression.

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The levels of cell free circulating tumor DNA (ctDNA) in plasma correlated with treatment response and outcome in systemic lymphomas. Notably, in brain tumors, the levels of ctDNA in the cerebrospinal fluid (CSF) are higher than in plasma. Nevertheless, their role in central nervous system (CNS) lymphomas remains elusive.

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Cancer response to immunotherapy depends on the infiltration of CD8 T cells and the presence of tumor-associated macrophages within tumors. Still, little is known about the determinants of these factors. We show that LIF assumes a crucial role in the regulation of CD8 T cell tumor infiltration, while promoting the presence of protumoral tumor-associated macrophages.

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Purpose: We investigated the value of tumor-infiltrating NK (TI-NK) cells and HLA class I tumor expression as biomarkers of response to neoadjuvant anti-HER2 antibody-based treatment in breast cancer.

Experimental Design: TI-NK cells and HLA-I were determined by IHC in pretreatment tumor biopsies from two cohorts of patients with HER2-positive breast cancer [discovery cohort ( = 42) and validation cohort ( = 71)]. Tumor-infiltrating lymphocytes (TIL) were scored according to international guidelines.

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Throughout their development, tumors are challenged by the immune system, and they acquire features to evade its surveillance. A systematic view of these traits, which shed light on how tumors respond to immunotherapies, is still lacking. Here, we computed the relative abundance of an array of immune cell populations to measure the immune infiltration pattern of 9,174 tumors of 29 solid cancers.

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Identifying molecular cancer drivers is critical for precision oncology. Multiple advanced algorithms to identify drivers now exist, but systematic attempts to combine and optimize them on large datasets are few. We report a PanCancer and PanSoftware analysis spanning 9,423 tumor exomes (comprising all 33 of The Cancer Genome Atlas projects) and using 26 computational tools to catalog driver genes and mutations.

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Diffuse gliomas are the most common primary tumor of the brain and include different subtypes with diverse prognosis. The genomic characterization of diffuse gliomas facilitates their molecular diagnosis. The anatomical localization of diffuse gliomas complicates access to tumor specimens for diagnosis, in some cases incurring high-risk surgical procedures and stereotactic biopsies.

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While tumor genome sequencing has become widely available in clinical and research settings, the interpretation of tumor somatic variants remains an important bottleneck. Here we present the Cancer Genome Interpreter, a versatile platform that automates the interpretation of newly sequenced cancer genomes, annotating the potential of alterations detected in tumors to act as drivers and their possible effect on treatment response. The results are organized in different levels of evidence according to current knowledge, which we envision can support a broad range of oncology use cases.

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Article Synopsis
  • Understanding disease comorbidities has crucial socio-economic impacts, influencing areas like clinical research and healthcare strategy.
  • This study uses an interactome-based approach to identify functional overlaps between diseases, revealing 206 significant links among 94 diseases, which highlights common inflammatory processes.
  • The research also examines how mutations affect protein interactions, offering insights into the relationships between diseases, including an inverse comorbidity between Alzheimer's disease and certain cancer types.
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Background: Profiling the somatic mutations of genes which may inform about tumor evolution, prognostics and treatment is becoming a standard tool in clinical oncology. Commercially available cancer gene panels rely on manually gathered cancer-related genes, in a "one-size-fits-many" solution. The design of new panels requires laborious search of literature and cancer genomics resources, with their performance on cohorts of patients difficult to estimate.

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Chronic lymphocytic leukaemia (CLL) is a frequent disease in which the genetic alterations determining the clinicobiological behaviour are not fully understood. Here we describe a comprehensive evaluation of the genomic landscape of 452 CLL cases and 54 patients with monoclonal B-lymphocytosis, a precursor disorder. We extend the number of CLL driver alterations, including changes in ZNF292, ZMYM3, ARID1A and PTPN11.

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Large efforts dedicated to detect somatic alterations across tumor genomes/exomes are expected to produce significant improvements in precision cancer medicine. However, high inter-tumor heterogeneity is a major obstacle to developing and applying therapeutic targeted agents to treat most cancer patients. Here, we offer a comprehensive assessment of the scope of targeted therapeutic agents in a large pan-cancer cohort.

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Extracting relevant information from large-scale data offers unprecedented opportunities in cancerology. We applied independent component analysis (ICA) to bladder cancer transcriptome data sets and interpreted the components using gene enrichment analysis and tumor-associated molecular, clinicopathological, and processing information. We identified components associated with biological processes of tumor cells or the tumor microenvironment, and other components revealed technical biases.

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Motivation: Several computational methods have been developed to identify cancer drivers genes-genes responsible for cancer development upon specific alterations. These alterations can cause the loss of function (LoF) of the gene product, for instance, in tumor suppressors, or increase or change its activity or function, if it is an oncogene. Distinguishing between these two classes is important to understand tumorigenesis in patients and has implications for therapy decision making.

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