Biology subjects

deCarvalho, A. C.

Publications and source records attributed to deCarvalho, A. C..

5 recordsLinked to original sources

bioRxiv2026

Local translation drives glioblastoma heterogeneity and tumor invasion

O_FIG O_LINKSMALLFIG WIDTH=174 HEIGHT=200 SRC="FIGDIR/small/722387v1_ufig1.gif" ALT="Figure 1"> View larger version (96K): org.highwire.dtl.DTLVardef@1da020corg.highwire.dtl.DTLVardef@1bc9b32org.highwire.dtl.DTLVardef@171198aorg.highwire.dtl.DTLVardef@43ce6f_HPS_FORMAT_FIGEXP M_FIG C_FIG Glioblastoma is characterized by diffuse brain invasion, yet the subcellular mechanisms enabling this aggressive behavior remain poorly understood. A subpopulation of glioblastoma cells forms invasive tumor microtubes (TMs), neurite-like extensions that drive whole-brain colonization. Here, we establish local protein translation as a fundamental driver of TM dynamics and invasive cell states. Developing a subcellular transcriptomics approach - integrating subcellular organelle organization with spatially resolved transcriptomics and functional readouts - we reveal that TM gene expression drives cell state identity. Invasive cells further exhibit significantly elevated local translation in protruding TMs, directly linking subcellular protein synthesis to functional invasive states associated with neurodevelopmental programs of axonal growth cones. Targeted disruption of TM-localized translation via photoswitchable puromycin, and specific knockdowns of the TM-enriched proteins GPM6A and GAP43, impaired TM dynamics, suppressed invasion, and reduced tumor growth. Together, these findings define local translation as a key determinant of tumor heterogeneity and glioblastoma invasion.

cancer biology↗

bioRxiv2024

Profiling Glioma Stem Cell Dynamics via 3D-based Cell Cycle Reporter Assays

Successful containment of unwanted cell cycle progression in tumours such as glioblastoma (GBM) requires targeted therapeutic approaches which rely on understanding cell cycle dynamics in response to microenvironmental stimuli. Glioma Stem Cells (GSCs) can drive tumour initiation, recurrence, therapy resistance, and are often attributed to the heterogeneity and plasticity of GBM. In vitro models using patient-derived GSCs provide a life relevant tool for exploration of complex molecular mechanisms underlying the aggressive characteristics of GBM. Introduction of 3D tissue culture systems permits the study of spatial complexity of the tumour mass and enables control over diverse conditions within the surrounding microenvironment. This chapter demonstrates detailed methods to study spatio-temporal changes to the cell cycle dynamics using available fluorescent cell cycle reporter systems in combination with bioinformatics-based signal intensity and localization analysis. We present a successful approach that investigates the 3D cell cycle dynamics of GSC populations. This approach utilizes GBM neurosphere and organoid cultures, which are assessed over time and under therapeutic pressure. These models can be further explored, manipulated, and customized to serve specific experimental designs.

cancer biology↗

bioRxiv2020

DNA Methylation-based Signatures Classify Sporadic Pituitary Tumors According to Clinicopathological Features.

BackgroundDistinct genome-wide methylation patterns have consistently clustered pituitary neuroendocrine tumors (PT) into molecular groups associated with specific clinicopathological features. Here we aim to identify, characterize and validate the methylation signatures that objectively classify PT into those molecular groups. MethodsCombining in-house and publicly available data, we conducted an analysis of the methylome profile of a comprehensive cohort of 177 tumor and 20 non-tumor specimens from the pituitary gland. We also retrieved methylome data from an independent pituitary tumor (PT) cohort (N=86) to validate our findings. ResultsWe identified three methylation clusters associated with functional status and adenohypophyseal cell lineages using an unsupervised approach. We also identified signatures based on differentially methylated CpG probes (DMP), some of which overlapped with pituitary-specific transcription factors genes (SF1 and Tpit), that significantly distinguished pairs of clusters related to functional status and adenohypophyseal cell lineage. These findings were reproduced in an independent cohort, validating these methylation signatures. The DMPs were mainly annotated in enhancer regions associated with pathways and genes involved in cell identity and tumorigenesis. ConclusionsWe identified and validated methylation signatures that distinguished PT by distinct functional status and adenohypophyseal cell lineages. These signatures, annotated in enhancer regions, indicate the importance of these elements in pituitary tumorigenesis. They also provide an unbiased approach to classify pituitary tumors according to the most recent classification recommended by the WHO 2017 using methylation profiling. Key-pointsO_LIDistinct methylation landscapes define PT groups with specific functional status/subtypes and adenohypophyseal lineages subtypes. C_LIO_LIMethylation abnormalities in each cluster mainly occur in CpG annotated in distal regions overlapping predicted enhancers regions associated with pathways and genes involved in cell identity and tumorigenesis. C_LIO_LIDNA methylation signatures provide an unbiased approach to classify PT. C_LI Importance of the studyThis study harnessed the largest methylome data to date from a comprehensive cohort of pituitary specimens obtained from four different institutions. We identified and validated methylation signatures that distinguished pituitary tumors into molecular groups that reflect the functionality and adenohypophyseal cell lineages of these tumors. These signatures, mainly located in enhancers, are associated with pathways and genes involved in cell identity and tumorigenesis. Our results show that methylome profiling provides an objective approach to classify PT according to the most recent classification of PT recommended by the 2017 WHO.

cancer biology↗

bioRxiv2017

Distinct epigenetic shift in a subset of Glioma CpG island methylator phenotype (G-CIMP) during tumor recurrence

Histomorphology and current grading schemes are unable to predict glioma relapse and malignant tumor progression. We reported that the IDH-mutant associated Glioma-CpG Island Methylator Phenotype (G-CIMP) can be further divided into two clinically distinct subtypes independent of histopathological grading (G-CIMP-high and -low) with evidence of correlation with tumor progression. Here we performed a comprehensive epigenomic analysis of 74 longitudinally collected glioma samples (grade II-IV) to understand malignant recurrence from G-CIMP-high to G-CIMP-low. G-CIMP-low recurrence appeared in 12% of all gliomas and resemble IDH-wildtype primary glioblastoma. G-CIMP-low recurrence can be characterized by distinct epigenetic changes at candidate functional tissue enhancers with AP-1/SOX binding elements, stem cell-like epigenomic phenotype, and genomic instability. Finally, we defined a set of candidate biomarker signatures that predict recurrence of G-CIMP-low with clinically relevance on patient outcomes. Our study provides opportunity for refined clinical trial designs and therapeutic targets that limit progression to more aggressive G-CIMP-low phenotype.\n\nHIGHLIGHTSO_LIIndolent G-CIMP-high progresses to aggressive G-CIMP-low phenotype\nC_LIO_LIIncidence of G-CIMP-low recurrent tumors are 3 times greater than G-CIMP-low primary\nC_LIO_LIG-CIMP-low recurrent tumors share epigenomic features with IDH-wildtype primary GBM\nC_LIO_LIPredictive biomarkers of G-CIMP-low progression at primary diagnosis\nC_LI

cancer biology↗

bioRxiv2016

Extrachromosal DNA elements can drive disease evolution in glioblastoma

To understand how genomic heterogeneity of glioblastoma (GBM) contributes to the poor response to therapy, which is characteristic of this disease, we performed DNA and RNA sequencing on GBM tumor samples and the neurospheres and orthotopic xenograft models derived from them. We used the resulting data set to show that somatic driver alterations including single nucleotide variants, focal DNA alterations, and oncogene amplification in extrachromosomal DNA (ecDNA) elements were in majority propagated from tumor to model systems. In several instances, ecDNAs and chromosomal alterations demonstrated divergent inheritance patterns and clonal selection dynamics during cell culture and xenografting. Longitudinal patient tumor profiling showed that oncogenic ecDNAs are frequently retained after disease recurrence. Our analysis shows that extrachromosomal elements increase the genomic heterogeneity during tumor evolution of glioblastoma, independent of chromosomal DNA alterations.

cancer biology↗