Biology subjects

Malta, T. M.

Publications and source records attributed to Malta, T. M..

12 recordsLinked to original sources

bioRxiv2026

Point-Process Modelling of Cell-Type Interaction Structure in CosMx Glioma Data for Hypothesis-Driven Tumor Inference from scRNA-seq

Glioblastoma (GBM) exhibits marked spatial heterogeneity that is lost after tissue dissociation for single-cell RNA sequencing. Here, we developed a spatial-statistical framework to characterize tumor-microenvironment organization and derive molecularly predictable spatial phenotypes from CosMx data. Eight GBM specimens comprising 2,427,362 cells, 15 cell populations, and four malignant states were analyzed, revealing substantial inter-patient differences in cellular composition and density. Fine finite-element meshes best preserved continuous spatial intensity and supported whole-tissue Log-Gaussian Cox Process modelling. Immune populations, including macrophages, microglia, monocytes, cDCs, neutrophils, and T cells, showed spatial attraction toward malignant states. These models generated cell-level measures of conditional intensity and tumor-proximity probability. Transcript abundances and metabolic pathway scores served as the sole predictive features to model these spatial phenotypes. Transcriptomic representations outperformed metabolic pathway scores. Leave-one-patient-out validation reduced predictive performance, and patient-wise harmonization failed to restore cross-patient transportability. Spatial outcome distributions varied between specimens, indicating that molecular-spatial relationships are strongly conditioned by patient-specific tumor architecture. Predicted interaction scores stratified transcriptional subclusters within cell types in external non-spatial single-cell datasets. This framework links spatial point-process phenotypes to molecular states and provides a basis for inferring spatial context from non-spatial single-cell data. Generalizing these models requires larger and more diverse spatial cohorts.

bioinformatics↗

bioRxiv2025

Integrative multi-omics combined with functional pharmacological profiling in patient-derived organoids identifies personalized therapeutic vulnerabilities of adult high-grade gliomas

BackgroundPrecision medicine has transformed cancer treatment by tailoring therapies to specific molecular aberrations. Integrating high-resolution multi-omics with high-throughput functional profiling in patient-derived organoids of-fers a powerful strategy to further refine patient stratification. While (epi)genetic profiling has drastically improved the classification in diffuse adult gliomas, these advances have not yet translated into effective therapeutic interventions and precision medicine approaches remain to be established. Material and MethodsWe investigated a panel of 48 patient-derived organoid and orthotopic xenograft models of adult high-grade gliomas, comprehensively characterized at genomic, epigenomic and transcriptomic levels. A functional drug screen was performed on 27 organoid models using a 202-compound library targeting cancer-related pathways and epigenetic regulators. Unsupervised multi-omics factor analysis was employed to identify patient-specific therapeutic vulnerabilities. Validation included dose-dependent drug efficacy assessments, as well as biomarker assessment in patient tumors across molecular subgroups. ResultsMulti-omics analysis revealed a broad spectrum of molecular profiles capturing the genetic, epigenetic, and transcriptomic diversity of high-grade gliomas. Multi-omics factor analysis, integrating multi-omics and drug response profiles, identified distinct subgroups associated with IDH1 mutation and MYCN amplification. IDH1 mutant grade 4 astrocytomas showed selective sensitivity to histone deacetylase 3 inhibitors, while a MYCN-amplified glioblastoma responded preferentially to histone methyltransferase inhibitors. The differential drug responses were linked to specific (epi)genetic and transcriptomic biomarkers. While other glioblastomas exhibited heterogeneous treatment responses, no robust biomarker-defined responder subgroups were identified. ConclusionOur findings highlight the value of integrating multi-omics and functional profiling to inform precision medicine strategies. This approach enables the stratification of distinct patient subgroups in preclinical models, paving the way for tailored therapeutic interventions. While we observed distinct pharmacogenomic profiles in IDH1 mutant grade 4 astrocytomas and a MYCN-amplified glioblastoma, implementing precision medicine in other glioblastoma subtypes remains a substantial challenge. Key pointsO_LIIntegrating drug screening in a panel of patient-derived organoids with multi-omics enables pharmacogenomic profiling in adult diffuse high-grade gliomas C_LIO_LIIDH1 mutant grade 4 astrocytomas are sensitive to histone deacetylase 3 inhibitors C_LIO_LIMYCN-amplified glioblastoma exhibits distinct DNA methylation pattern and drug responses C_LI Study importanceTo date, attempts to develop effective precision medicine in adult high-grade gliomas failed. Here, we provide a preclinical framework for identifying personalized therapeutic by integrating multi-omics profiling with functional drug screening in patient-derived organoids. We show that IDH1 mutant high-grade astrocytomas present distinct therapeutic vulnerabilities compared to glioblastomas, linked to sensitivity to histone deacetylase 3 inhibitors. Within glioblastomas, we identified a distinct MYCN-amplified tumor, sensitive to histone methyltransferase inhibitors. Applying pharmacogenomic approaches using novel drug libraries holds promise for uncovering additional clinically relevant patient subgroups in the future. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=104 SRC="FIGDIR/small/675145v1_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@12c04f9org.highwire.dtl.DTLVardef@fa8872org.highwire.dtl.DTLVardef@141de6org.highwire.dtl.DTLVardef@b72086_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2024

Stemness prediction models reveal glioma aggressiveness and therapeutic targets in gliomas

Gliomas are complex and heterogeneous primary brain tumors with a high degree of therapeutic resistance, particularly glioblastomas, which carry a poor prognosis. Cellular plasticity and stem cell-like features, or "stemness," are increasingly recognized as key contributors to tumor progression and treatment resistance. In this study, we introduce two machine-learning-based prediction models designed to assess stemness in glioma samples using bulk gene expression data. One model captures fetal astrocyte characteristics (ASTsi), while the other identifies glioma stem cell traits (GSCsi). ASTsi was notably correlated with poor prognosis in IDHmut gliomas, whereas GSCsi was more indicative of stemness in IDHwt subtypes. Longitudinal data analysis showed that IDHwt and grade IV gliomas exhibit shifts in stemness indices upon recurrence, suggesting a phenotypic change linked to therapy resistance. Additionally, single-cell transcriptomic analysis confirmed that GSCsi can detect stem-like cell subsets in IDHwt gliomas. This approach enhances our understanding of glioma heterogeneity and reveals potential therapeutic targets.

bioinformatics↗

bioRxiv2023

Machine learning determines stemness associated with simple and basal-like canine mammary carcinomas

Simple and complex carcinomas are the most common type of malignant Canine Mammary Tumors (CMTs), with simple carcinomas exhibiting aggressive behavior and poorer prognostic. Stemness is an ability associated with cancer initiation and progression, malignancy, and therapeutic resistance, but is still few elucidated in different canine mammary cancer subtypes. Here, we first validated, using CMT samples, a previously published canine one-class logistic regression machine learning algorithm (OCLR) to predict stemness (mRNAsi) in canine cancer cells. Then, we observed that simple carcinomas exhibit higher stemness than other histological subtypes and confirmed that stemness is higher and associated with basal-like tumors and with NMF2 tumor-specific metagene signature. Furthermore, using correlation analysis, we suggested two promise stemness-associated targets in CMTs, POLA2 and APEX1, especially in simple canine mammary tumors. Thus, our work elucidates stemness as a potential mechanism behind the aggressiveness and development of simple canine mammary tumors, describing novel pieces of evidence of a promising strategy to target canine mammary carcinomas, especially the simple subtype.

cancer biology↗

bioRxiv2021

The epigenetic evolution of gliomas is determined by their IDH1 mutation status and treatment regimen

Tumor adaptation or selection is thought to underlie therapy resistance of gliomas. To investigate the longitudinal epigenetic evolution of gliomas in response to therapeutic pressure, we performed an epigenomic analysis of 143 matched initial and recurrent patients with IDH-wildtype (IDHwt) and IDH-mutant (IDHmut) gliomas. IDHwt gliomas showed a longitudinally stable epigenome with relatively low levels of global methylation, whereas the epigenome of IDHmut gliomas showed initial high levels genome-wide of DNA methylation that was progressively reduced to levels similar to those of IDHwt tumors. By integrating DNA methylation and gene expression data, adaptive changes of putative master regulators of the cell cycle and of differentiation were seen in IDHmut recurrent tumors. Furthermore, relapses of IDHmut tumors were accompanied by histological progression which in turn influenced survival, as validated in an independent cohort. Finally, the initial cell composition of the tumor microenvironment differed between IDHwt and IDHmut tumors and changed differentially following treatment, suggesting increased neo-angiogenesis and T-cell infiltration upon treatment for IDHmut gliomas. Our study provides one of the largest cohorts of paired glioma samples profiled with epigenomics, transcriptomics and genomics; and our results demonstrate that the treatment of IDHmut gliomas reshapes the epigenome towards an IDHwt-like phenotype. Accordingly, the prevalent practice of early genotoxic treatment in this patient population may need to be revisited.

genomics↗

bioRxiv2021

Longitudinal analysis of diffuse glioma reveals cell state dynamics at recurrence associated with changes in genetics and the microenvironment

To interrogate the factors driving therapy resistance in diffuse glioma, we collected and analyzed RNA and/or DNA sequencing data from temporally separated tumor pairs of 292 adult patients with IDH-wild-type or IDH-mutant glioma. Tumors recurred in distinct manners that were dependent on IDH mutation status and attributable to changes in histological feature composition, somatic alterations, and microenvironment interactions. Hypermutation and acquired CDKN2A deletions associated with an increase in proliferating stem-like malignant cells at recurrence in both glioma subtypes, reflecting active tumor growth. IDH-wild-type tumors were more invasive at recurrence, and their malignant cells exhibited increased expression of neuronal signaling programs that reflected a possible role for neuronal interactions in promoting glioma progression. Mesenchymal transition was associated with the presence of a specific myeloid cell state defined by unique ligand-receptor interactions with malignant cells. Collectively, our results uncover recurrence-associated changes that could be targetable to shape disease progression following initial diagnosis.

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↗

bioRxiv2020

Primary and recurrent glioma patient-derived orthotopic xenografts (PDOX) represent relevant patient avatars for precision medicine

Patient-derived cancer models are essential tools for studying tumor biology and preclinical interventions. Here, we show that glioma patient-derived orthotopic xenografts (PDOXs) enable long-term propagation of patient tumors and represent clinically relevant patient avatars. We created a large collection of PDOXs from primary and recurrent gliomas with and without mutations in IDH1, which retained histopathological, genetic, epigenetic and transcriptomic features of patient tumors with no mouse-specific clonal evolution. Longitudinal PDOX models recapitulate the limited genetic evolution of gliomas observed in patient tumors following treatment. PDOX-derived standardized tumor organoid cultures enabled assessment of drug responses, which were validated in mice. PDOXs showed clinically relevant responses to Temozolomide and to targeted treatments such as EGFR and CDK4/6 inhibitors in (epi)genetically defined groups, according to MGMT promoter and EGFR/CDK status respectively. Dianhydrogalactitol, a bifunctional alkylating agent, showed promising potential against glioblastoma. Our study underlines the clinical relevance of glioma PDOX models for translational research and personalized treatment studies.

cancer biology↗

bioRxiv2019

Predicting master transcription factors from pan-cancer expression data

The function of critical developmental regulators can be subverted by cancer cells to control expression of oncogenic transcriptional programs. These "master transcription factors" (MTFs) are often essential for cancer cell survival and represent vulnerabilities that can be exploited therapeutically. The current approaches to identify candidate MTFs examine super-enhancer associated transcription factor-encoding genes with high connectivity in network models. This relies on chromatin immunoprecipitation-sequencing (ChIP-seq) data, which is technically challenging to obtain from primary tumors, and is currently unavailable for many cancer types and clinically relevant subtypes. In contrast, gene expression data are more widely available, especially for rare tumors and subtypes where MTFs have yet to be discovered. We have developed a predictive algorithm called CaCTS (Cancer Core Transcription factor Specificity) to identify candidate MTFs using pan-cancer RNA-sequencing data from The Cancer Genome Atlas. The algorithm identified 273 candidate MTFs across 34 tumor types and recovered known tumor MTFs. We also made novel predictions, including for cancer types and subtypes for which MTFs have not yet been characterized. Clustering based on MTF predictions reproduced anatomic groupings of tumors that share 1-2 lineage-specific candidates, but also dictated functional groupings, such as a squamous group that comprised five tumor subtypes sharing 3 common MTFs. PAX8, SOX17, and MECOM were candidate factors in high-grade serous ovarian cancer (HGSOC), an aggressive tumor type where the core regulatory circuit is currently uncharacterized. PAX8, SOX17, and MECOM are required for cell viability and lie proximal to super-enhancers in HGSOC cells. ChIP-seq revealed that these factors co-occupy HGSOC regulatory elements globally and co-bind at critical gene loci including MUC16 (CA-125). Addiction to these factors was confirmed in studies using THZ1 to inhibit transcription in HGSOC cells, suggesting early down-regulation of these genes may be responsible for cytotoxic effects of THZ1 on HGSOC models. Identification of MTFs across 34 tumor types and 140 subtypes, especially for those with limited understanding of transcriptional drivers paves the way to therapeutic targeting of MTFs in a broad spectrum of cancers.

cancer biology↗

bioRxiv2019

Detection of glioma and prognostic subtypes by non-invasive circulating cell-free DNA methylation markers

Genome-wide DNA methylation profiling has shown that epigenetic abnormalities are biologically important in glioma and can be used to classify these tumors into distinct prognostic groups. Thus far, DNA profiling has required surgically resected glioma tissue; however, gliomas release tumoral material into biofluids, such as blood and cerebrospinal fluid, providing an opportunity for a minimally invasive testing. While prior studies have shown that genetic and epigenetic markers can be detected in blood or cerebrospinal fluid (e.g., liquid biopsy [LB]), there has been low sensitivity for tumor-specific markers. We hypothesize that the low sensitivity is due to the targeted assay methods. Therefore, we profiled the genome-wide CpG methylation levels in DNA of tumor tissue and cell-free DNA in serum of glioma patients, to identify non-invasive epigenetic LB (eLB) markers in the serum that reflect the characteristics of the tumor tissue. From the epigenetic profiles of serum from patients diagnosed with glioma (N=15 IDH mutant and N=7 IDH wildtype) and with epilepsy (N=3), we defined glioma-specific and IDH-specific eLB signatures (Glioma-eLB and IDH-eLB, respectively). The epigenetic profiles of the matched tissue demonstrate that these eLB signatures reflected the signature of the tumor. Through cross-validation we show that Glioma-eLB can accurately predict a patients glioma from those with other neoplasias (N=6 Colon; N=14 Pituitary; N=3 Breast; N=4 Lung), non-neoplastic immunological conditions (N=22 sepsis; N=9 pancreatic islet transplantation), and from healthy individuals (sensitivity: 98%; specificity: 99%). Finally, IDH-eLB includes promoter methylated markers associated with genes known to be involved in glioma tumorigenesis (PVT1 and CXCR6). The application of the non-invasive eLB signature discovered in this study has the potential to complement the standard of care for patients harboring glioma.

cancer biology↗

bioRxiv2017

Glioma CpG Island Methylator Phenotype (G-CIMP): Biological and Clinical Implications

Gliomas are a heterogeneous group of brain tumors with distinct biological and clinical properties. Despite advances in surgical techniques and clinical regimens, treatment of high-grade glioma remains challenging and carries dismal rates of therapeutic success and overall survival. Challenges include the molecular complexity of gliomas, as well as inconsistencies in histopathological grading, resulting in an inaccurate prediction of disease progression and failure in the use of standard therapy. The updated 2016 World Health Organization (WHO) classification of tumors of the central nervous system reflects a refinement of tumor diagnostics by integrating the genotypic and phenotypic features, thereby narrowing the defined subgroups. The new classification recommends the molecular diagnosis of the IDH mutational status in gliomas. IDH-mutant gliomas manifest the CpG Island Methylator Phenotype (G-CIMP). Notably, the recent identification of clinically relevant subsets of G-CIMP tumors (G-CIMP-high and G-CIMP-low) provide a further refinement in glioma classification that is independent of grade and histology. This scheme may be useful for predicting patient outcome and may be translated into effective therapeutic strategies tailored to each patient. In this review, we highlight the evolution of our understanding of the G-CIMP subsets and how recent advances in characterizing the genome and epigenome of gliomas may influence future basic and translational research.\n\nConflict of interestThe authors declare no conflict of interest

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↗