Relevant bibliographies by topics / Intra-Tumoral heterogeneity

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  2. Dissertations / Theses
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Academic literature on the topic 'Intra-Tumoral heterogeneity'

Author: Grafiati
Published: 25 January 2025

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Journal articles on the topic "Intra-Tumoral heterogeneity"

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Rybinski, Brad, and Kyuson Yun. "Addressing intra-tumoral heterogeneity and therapy resistance." Oncotarget 7, no. 44 (September 6, 2016): 72322–42. http://dx.doi.org/10.18632/oncotarget.11875.

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Korpershoek, Esther, Claudia K. Stobbe, Francien H. van Nederveen, Ronald R. de Krijger, and Winand N. M. Dinjens. "Intra-tumoral molecular heterogeneity in benign and malignant pheochromocytomas and extra-adrenal sympathetic paragangliomas." Endocrine-Related Cancer 17, no. 3 (September 2010): 653–62. http://dx.doi.org/10.1677/erc-10-0072.

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Pheochromocytomas (PCCs) and extra-adrenal sympathetic paragangliomas (sPGLs) are catecholamine-producing tumors occurring in the context of hereditary tumor syndromes, with known germline mutations, and as sporadic tumors. The pathogenesis of sporadic PCC and sPGL is poorly understood, and little is known about intra-tumoral heterogeneity with respect to molecular aberrations. Since knowledge on intra-tumoral heterogeneity is important for understanding the pathogenesis of these tumors, we investigated 12 benign and 8 malignant PCCs and sPGLs for loss of heterozygosity (LOH) on DNA extracted from different regions of each tumor and from metastases. LOH markers were selected on chromosomal regions frequently deleted in PCC, including 1p, 3q, 3p, and 11p. Benign tumors were found to have less intra-tumoral heterogeneity (overall 8%) than malignant tumors (overall 23%), with the highest frequencies for chromosome 1p36 in the benign tumors (17%) and 1p13 and 3q24 in malignant tumors (both 38%). In addition, differences in LOH patterns were detected between paired primary malignant tumors, and their metastases and different LOH patterns were observed in bilateral PCC of a multiple endocrine neoplasia type 2 patient. We demonstrate that malignant PCC and sPGL have more intra-tumoral molecular heterogeneity than benign tumors, which suggests that benign and malignant PCC and sPGL have a different pathogenesis.
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Landau, Dan A. "Epigenetic Heterogeneity in Non-Hodgkin Lymphoma." Blood 132, Supplement 1 (November 29, 2018): SCI—35—SCI—35. http://dx.doi.org/10.1182/blood-2018-99-109500.

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Abstract In chronic lymphocytic leukemia (CLL) genetically distinct subpopulations are commonly observed and predict future evolutionary and clinical trajectories. Similar to the case of intra-tumoral genetic diversity, intra-tumoral epigenetic diversity occurs at the level of DNA methylation, leading to massive stochastic diversification in methylation patterns. Importantly, stochastic "epimutations" impact transcription, clonal evolution and clinical outcome. To robustly differentiate "epidrivers" from the majority of random passenger DNA methylation changes, a novel statistical inference framework has been developed that accounts for the varying epimutation rate across the genome. This framework can also include histone modifications revealing a decrease in the coherence between different epigenetic marks in CLL and consistent with intra-leukemic epigenetic diversity. Finally, multi-omic single-cell sequencing techniques further allow for tracking of epimutations and inferring high-resolution lineage trees. Disclosures Landau: Pharmacylcics: Honoraria.
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Wang, Lujia, Hairong Wang, Fulvio D’Angelo, Lee Curtin, Christopher P. Sereduk, Gustavo De Leon, Kyle W. Singleton, et al. "Quantifying intra-tumoral genetic heterogeneity of glioblastoma toward precision medicine using MRI and a data-inclusive machine learning algorithm." PLOS ONE 19, no. 4 (April 3, 2024): e0299267. http://dx.doi.org/10.1371/journal.pone.0299267.

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Background and objective Glioblastoma (GBM) is one of the most aggressive and lethal human cancers. Intra-tumoral genetic heterogeneity poses a significant challenge for treatment. Biopsy is invasive, which motivates the development of non-invasive, MRI-based machine learning (ML) models to quantify intra-tumoral genetic heterogeneity for each patient. This capability holds great promise for enabling better therapeutic selection to improve patient outcome. Methods We proposed a novel Weakly Supervised Ordinal Support Vector Machine (WSO-SVM) to predict regional genetic alteration status within each GBM tumor using MRI. WSO-SVM was applied to a unique dataset of 318 image-localized biopsies with spatially matched multiparametric MRI from 74 GBM patients. The model was trained to predict the regional genetic alteration of three GBM driver genes (EGFR, PDGFRA and PTEN) based on features extracted from the corresponding region of five MRI contrast images. For comparison, a variety of existing ML algorithms were also applied. Classification accuracy of each gene were compared between the different algorithms. The SHapley Additive exPlanations (SHAP) method was further applied to compute contribution scores of different contrast images. Finally, the trained WSO-SVM was used to generate prediction maps within the tumoral area of each patient to help visualize the intra-tumoral genetic heterogeneity. Results WSO-SVM achieved 0.80 accuracy, 0.79 sensitivity, and 0.81 specificity for classifying EGFR; 0.71 accuracy, 0.70 sensitivity, and 0.72 specificity for classifying PDGFRA; 0.80 accuracy, 0.78 sensitivity, and 0.83 specificity for classifying PTEN; these results significantly outperformed the existing ML algorithms. Using SHAP, we found that the relative contributions of the five contrast images differ between genes, which are consistent with findings in the literature. The prediction maps revealed extensive intra-tumoral region-to-region heterogeneity within each individual tumor in terms of the alteration status of the three genes. Conclusions This study demonstrated the feasibility of using MRI and WSO-SVM to enable non-invasive prediction of intra-tumoral regional genetic alteration for each GBM patient, which can inform future adaptive therapies for individualized oncology.
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Wu, Qiong, Anders E. Berglund, Robert J. Macaulay, and Arnold B. Etame. "The Role of Mesenchymal Reprogramming in Malignant Clonal Evolution and Intra-Tumoral Heterogeneity in Glioblastoma." Cells 13, no. 11 (May 30, 2024): 942. http://dx.doi.org/10.3390/cells13110942.

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Glioblastoma (GBM) is the most common yet uniformly fatal adult brain cancer. Intra-tumoral molecular and cellular heterogeneities are major contributory factors to therapeutic refractoriness and futility in GBM. Molecular heterogeneity is represented through molecular subtype clusters whereby the proneural (PN) subtype is associated with significantly increased long-term survival compared to the highly resistant mesenchymal (MES) subtype. Furthermore, it is universally recognized that a small subset of GBM cells known as GBM stem cells (GSCs) serve as reservoirs for tumor recurrence and progression. The clonal evolution of GSC molecular subtypes in response to therapy drives intra-tumoral heterogeneity and remains a critical determinant of GBM outcomes. In particular, the intra-tumoral MES reprogramming of GSCs using current GBM therapies has emerged as a leading hypothesis for therapeutic refractoriness. Preventing the intra-tumoral divergent evolution of GBM toward the MES subtype via new treatments would dramatically improve long-term survival for GBM patients and have a significant impact on GBM outcomes. In this review, we examine the challenges of the role of MES reprogramming in the malignant clonal evolution of glioblastoma and provide future perspectives for addressing the unmet therapeutic need to overcome resistance in GBM.
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Matsumoto, Yuji, Omkar Singh, Jose Garcia, Sunwoo Kwak, Zied Abdullaev, Nelson Freeburg, Dana Silverbush, Kenneth Aldape, Christos Davatzikos, and MacLean Nasrallah. "EPCO-15. INTRA-TUMORAL HETEROGENEITY OF DNA METHYLATION PROFILING AND CELLULAR COMPOSITION IN GLIOBLASTOMA." Neuro-Oncology 26, Supplement_8 (November 1, 2024): viii4. http://dx.doi.org/10.1093/neuonc/noae165.0014.

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Abstract BACKGROUND DNA methylation profiling has become an important diagnostic and exploratory tool in neuro-oncology. Intra-tumoral molecular heterogeneity is a crucial factor in the treatment resistance of IDH-wildtype glioblastoma (GBM). However, the intra-tumoral heterogeneity of methylation profiles in GBM remains poorly elucidated. This study aimed to investigate the intra-tumoral heterogeneity of DNA methylation subclasses and deconvolve methylation profiles to infer the cellular composition of the tumor. METHODS We conducted genome-wide DNA methylation analysis and applied the DKFZ/Heidelberg CNS tumor classifier to FFPE tissue samples from a multi-sampling cohort with multiple samples per patient and a single-sampling cohort with one sample per patient. Only samples classified as GBM were included. We conducted two different methylation-based deconvolution analyses. Deconvolution 1 inferred the fractions of tumor subtypes (RTK_I, RTK_II, MES_TYP, and MES_ATYP) and cell types in the microenvironment. Deconvolution 2 estimated the abundance of malignant cell states (stem-like and differentiated cell components) and cell types in the microenvironment. RESULTS The multi-sampling cohort included 32 patients with 113 samples, and the single-sampling cohort included 87 patients with 87 samples. In the multi-sampling cohort, 8 patients (25%) exhibited intra-patient heterogeneity in methylation subclasses across their samples. Deconvolution 1 indicated that each tumor subclass component was heterogeneously distributed within samples, and the abundance of the myeloid cell component correlated with the MES_TYP component across samples. Deconvolution 2 similarly revealed heterogeneity in the immune component abundance across samples within patients. Integrating the results of Deconvolution 1 and Deconvolution 2 using the entire cohort revealed significant correlations between RTK_I and stem-like, RTK_II and differentiated, and MES_TYP and immune components. These findings were also replicated in TCGA dataset. CONCLUSIONS Our findings underscore the importance of considering intra-tumoral heterogeneity in methylation profiles and the biological characteristics of each methylation subclass in developing novel GBM therapies.
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Schupp, Patrick, Samuel Shelton, Daniel Brody, Rebecca Eliscu, Brett Johnson, Tali Mazor, Kevin Kelley, et al. "EPCO-39. CLARIFYING THE MOLECULAR CONSEQUENCES OF ONCOGENIC MUTATIONS THROUGH MULTISCALE AND MULTIOMIC ANALYSIS OF INDIVIDUAL TUMORS." Neuro-Oncology 25, Supplement_5 (November 1, 2023): v132—v133. http://dx.doi.org/10.1093/neuonc/noad179.0501.

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Abstract Understanding the molecular consequences of oncogenic mutations is an important goal that may reveal essential features of malignant cells and new therapeutic targets for diverse cancers. However, this task is complicated by substantial inter- and intra-tumoral heterogeneity, which obscure the molecular consequences of oncogenic mutations in malignant cells. Although single-cell methods hold promise for this task, it remains non-trivial to isolate DNA and RNA from the same cell at scale. Here we describe a statistically motivated strategy that controls for inter-tumoral heterogeneity and exploits intra-tumoral heterogeneity to reveal the most consistent molecular phenotypes of malignant cells. By combining deep, multiscale sampling of IDH-mutant astrocytomas with integrative, multiomic analysis, we reconstruct and validate the phylogenies, spatial distributions, and molecular profiles of distinct malignant clones. Importantly, by genotyping nuclei for driver mutations, we show that existing strategies for inferring malignancy from gene expression profiles of single cells may be inaccurate. We identify genes that are consistently upregulated in the truncal clone of IDH-mutant astrocytomas, which are significantly enriched with genes involved in RNA splicing, mRNA transport and the nuclear pore, and WNT / MYC signaling. Furthermore, we find that expression phenotypes of malignancy persist despite loss of the mutant IDH1 protein following chr2q deletion in a subset of malignant cells. More broadly, our work provides a generalizable strategy for precisely deconstructing intra-tumoral heterogeneity and determining the most robust molecular consequences of oncogenic mutations in any kind of solid tumor.
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Moon, Chang-In, William Tompkins, Yuxi Wang, Abigail Godec, Xiaochun Zhang, Patrik Pipkorn, Christopher A. Miller, Carina Dehner, Sonika Dahiya, and Angela C. Hirbe. "Unmasking Intra-Tumoral Heterogeneity and Clonal Evolution in NF1-MPNST." Genes 11, no. 5 (May 1, 2020): 499. http://dx.doi.org/10.3390/genes11050499.

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Sarcomas are highly aggressive cancers that have a high propensity for metastasis, fail to respond to conventional therapies, and carry a poor 5-year survival rate. This is particularly true for patients with neurofibromatosis type 1 (NF1), in which 8%–13% of affected individuals will develop a malignant peripheral nerve sheath tumor (MPNST). Despite continued research, no effective therapies have emerged from recent clinical trials based on preclinical work. One explanation for these failures could be the lack of attention to intra-tumoral heterogeneity. Prior studies have relied on a single sample from these tumors, which may not be representative of all subclones present within the tumor. In the current study, samples were taken from three distinct areas within a single tumor from a patient with an NF1-MPNST. Whole exome sequencing, RNA sequencing, and copy number analysis were performed on each sample. A blood sample was obtained as a germline DNA control. Distinct mutational signatures were identified in different areas of the tumor as well as significant differences in gene expression among the spatially distinct areas, leading to an understanding of the clonal evolution within this patient. These data suggest that multi-regional sampling may be important for driver gene identification and biomarker development in the future.
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Servidei, Tiziana, Donatella Lucchetti, Pierluigi Navarra, Alessandro Sgambato, Riccardo Riccardi, and Antonio Ruggiero. "Cell-of-Origin and Genetic, Epigenetic, and Microenvironmental Factors Contribute to the Intra-Tumoral Heterogeneity of Pediatric Intracranial Ependymoma." Cancers 13, no. 23 (December 3, 2021): 6100. http://dx.doi.org/10.3390/cancers13236100.

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Intra-tumoral heterogeneity (ITH) is a complex multifaceted phenomenon that posits major challenges for the clinical management of cancer patients. Genetic, epigenetic, and microenvironmental factors are concurrent drivers of diversity among the distinct populations of cancer cells. ITH may also be installed by cancer stem cells (CSCs), that foster unidirectional hierarchy of cellular phenotypes or, alternatively, shift dynamically between distinct cellular states. Ependymoma (EPN), a molecularly heterogeneous group of tumors, shows a specific spatiotemporal distribution that suggests a link between ependymomagenesis and alterations of the biological processes involved in embryonic brain development. In children, EPN most often arises intra-cranially and is associated with an adverse outcome. Emerging evidence shows that EPN displays large intra-patient heterogeneity. In this review, after touching on EPN inter-tumoral heterogeneity, we focus on the sources of ITH in pediatric intra-cranial EPN in the framework of the CSC paradigm. We also examine how single-cell technology has shed new light on the complexity and developmental origins of EPN and the potential impact that this understanding may have on the therapeutic strategies against this deadly pediatric malignancy.
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Ramakrishnan, Valya, Gatikrushna Singh, Beibei Xu, Johnny Ackers, and Clark Chen. "DNAR-04. MICRORNA DEGRADATION ENHANCES GLIOBLASTOMA INTRA-TUMORAL HETEROGENEITY TO AUGMENT ACQUIRED TEMOZOLOMIDE RESISTANCE." Neuro-Oncology 25, Supplement_5 (November 1, 2023): v98—v99. http://dx.doi.org/10.1093/neuonc/noad179.0370.

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Abstract INTRODUCTION O6 methyl-guanine methyltransferase (MGMT) restores alkylated DNA to its undamaged form and dictates clonal evolution in response to chemotherapy. As such, MGMT is a major determinant of glioblastoma resistance to the standard-of-care chemotherapy temozolomide. miR-181d is a MGMT suppressing microRNA, whose expression inversely correlate with survival expectation after temozolomide treatment. Here, we characterize the expression of miR-181d and MGMT in response to temozolomide treatment. Method: Laboratory characterization, single cell analysis, and murine modeling RESULTS Treatment of glioblastoma cells with temozolomide (TMZ) induced a feed-forward cascade resulting in an ataxia telangiectasia and Rad3 (ATR) and polyribonucleotide nucleotidyl transferase 1 (PNPT1) dependent degradation of miR-181d. Single-cell analyses revealed such miR-181d degradation: 1) increased mean level of MGMT expression in a cell population and 2) enhanced cell-to-cell variability in MGMT expression to increase intra-tumoral heterogeneity in DNA repair capacity. This increased intra-tumoral heterogeneity enhanced glioblastoma resistance to temozolomide through a mechanism distinct from lowering the mean MGMT expression of the tumor population. Suppression of this heterogeneity by exogenous miR-181d over-expression enhanced the tumoricidal activity of temozolomide against glioblastomas. CONCLUSION We identified an interface between DNA damage response and miRNA degradation that mediate intra-tumoral heterogeneity and subsequent temozolomide resistance. This resistance mechanism is distinct from modulation of the mean MGMT expression of the population.
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Dissertations / Theses on the topic "Intra-Tumoral heterogeneity"

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Gopal, Priyanka. "THE IMPACT OF INTER- AND INTRA-TUMORAL HETEROGENEITY ON THETREATMENT OF CANCER." Case Western Reserve University School of Graduate Studies / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=case1554485746538973.

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Neuzillet, Cindy. "Inter- and intra-tumoral heterogeneity and dynamics of cancer-associated fibroblasts in pancreatic ductal adenocarcinoma." Thesis, Sorbonne Paris Cité, 2018. https://theses.md.univ-paris-diderot.fr/NEUZILLET_Cindy_2_va_20181015.zip.

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Les fibroblastes associés au cancer (cancer-associated fibroblasts, CAF) sont des chefs d’orchestre du microenvironnement tumoral (stroma) de l'adénocarcinome canalaire pancréatique (AP). L'hétérogénéité du stroma pourrait expliquer les rôles physiopathologiques non univoques (pro- vs. anti-tumoraux) du stroma de l’AP. Nous avons émis l'hypothèse qu'il existe plusieurs sous-types de CAFs dans l’AP qui contribuent à l'hétérogénéité du stroma notamment par leurs interactions avec les cellules tumorales et immunitaires.Ce projet s’est décomposé en trois parties :- Dans la première partie, en appliquant des analyses bioinformatiques étendues et un large éventail de tests in vitro à des cultures primaires de CAF dérivées d’AP, nous avons démontré la diversité biologique des CAFs pancréatiques humains; nous avons identifié quatre sous-types de CAFs avec des caractéristiques moléculaires et fonctionnelles spécifiques (signatures liées à la matrice et au système immunitaire, expression de vimentine et d’actine musculaire lisse, activité proliférative), et nous avons montré que l'hétérogénéité des CAFs avait un impact sur les interactions avec les cellules tumorales dans des modèles organotypiques.- Dans la deuxième partie, nous avons montré que les sous-types de CAFs et leurs combinaisons étaient associés à des caractéristiques phénotypiques distinctes des tumeurs (sous-type moléculaire et grade, abondance et activité du stroma, infiltrats immunitaires, angiogenèse) et à la survie des patients, in silico dans les données publiques de l'ICGC et par immunohistochimie dans une cohorte de patients bien caractérisés.- Dans la troisième partie, nous avons montré que plusieurs sous-types de CAFs peuvent émerger in vitro (expériences avec des milieux conditionnés) et in vivo (xénogreffes orthotopiques) à partir des cellules stellaires pancréatiques suite à leurs interactions dynamiques avec les cellules tumorales, par un processus d'«empreinte», modulé ensuite par d'autres facteurs et/ou partenaires cellulaires dans le microenvironnement tumoral; par ailleurs, nous avons confirmé dans un contexte murin nos résultats sur l'association entre l'expression des marqueurs de sous-types de CAFs et le phénotype immunitaire observé dans les tumeurs humaines. Cette classification unique des CAFs pancréatiques humains (pCAFassigner) démontre l'hétérogénéité phénotypique inter- et intra-tumorale des CAFs dans l’AP. Nos résultats ouvrent la voie à de futures études fonctionnelles et au développement de thérapies ciblant spécifiquement certaines sous-populations de CAFs dans l’AP
Cancer-associated fibroblasts (CAF) are orchestrators of the pancreatic ductal adenocarcinoma (PDAC) microenvironment. Stromal heterogeneity may explain differential pathophysiological roles of the stroma (pro- vs. anti-tumoral) in PDAC. We hypothesised that multiple CAF subtypes exist in PDAC that contribute to stromal heterogeneity through interactions with cancer and immune cells. This project comprised three parts:- In Part 1, by applying extended bioinformatics analysis and a wide range of in vitro assays to human PDAC-derived primary CAF cultures, we demonstrated the biological diversity of human pancreatic CAFs; we identified four CAF subtypes (A-D) with specific molecular and functional features (matrix- and immune-related signatures, vimentin and ?-smooth muscle actin expression, proliferation rate), and we showed that CAF heterogeneity had an impact on the interactions with cancer cells in mini-organotypic models.- In Part 2, we showed that the combination of CAF sub-populations was associated with distinct phenotypic characteristics of the tumours (tumour molecular subtype and grade, stromal abundance and activity, immune infiltrates, angiogenesis) and patient survival, in silico in the ICGC dataset and by immunohistochemistry in an extensively characterised patient cohort.- In Part 3, we showed that several CAF subtypes may emerge in vitro (conditioned media experiments) and in vivo (orthotopic xenografts) from the dynamic interactions of pancreatic stellate cells with cancer cells, through an “imprinting” process, and may be further modulated by other factors and/or cellular partners in the tumour microenvironment; in addition, we confirmed in a murine setting our findings about the association between CAF subtype marker expression and immune phenotype observed in human tumours.This unique classification for pancreatic CAFs (pCAFassigner) demonstrates the inter- and intra-tumoral phenotypic heterogeneity of CAFs in human PDAC. Our results provide a framework for future functional studies and pave the way for the development of therapies targeting specific CAF sub-populations in PDAC
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Sroussi, Marine. "Caractérisation de l’hétérogénéité intra-tumorale des cancers du côlon." Electronic Thesis or Diss., Université Paris sciences et lettres, 2024. http://www.theses.fr/2024UPSLS027.

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IntroductionL’hétérogénéité intra-tumorale est une des principales causes de résistance aux traitements, sa caractérisation aide à optimiser la prise en charge des patients. Dans ce contexte, l’un des enjeux majeurs est de pouvoir estimer finement la composition cellulaire des tissus tumoraux. Cela passe par la définition d’atlas cellulaires construits à partir du transcriptome de cellules uniques (scRNAseq). Ces atlas sont alors utilisés comme référence par des méthodes de déconvolution supervisée, afin d’estimer la composition cellulaire de tissus via l’analyse de leur transcriptome bulk. Les deux atlas cellulaires du cancer du côlon publiés à ce jour (Pelka 2021; Joanito 2022) sont discordants en termes de granularité et quant aux types de cellules tumorales identifiés. De plus, ils ne permettent pas une déconvolution robuste des données de transcriptome bulk ou spatial, n’étant pas construits dans cet objectif. Nous présentons un atlas cellulaire repensé des cellules du cancer du côlon, construit dans le but de permettre la déconvolution des données de transcriptome bulk ou spatial. Nous présentons également les méthodes et stratégies que nous avons développées pour construire ce nouvel atlas.MéthodesNous avons analysé les données scRNAseq publiques des études de Pelka et Joanito, totalisant plus de 600,000 cellules obtenues sur les tumeurs de plus de 120 patients. Nous avons d’abord construit deux sous-ensembles de ces données par tirage aléatoire, puis les avons analysés indépendamment, pour ne conserver que les résultats communs aux deux analyses. Nous avons ensuite réalisé une classification non supervisée en plusieurs étapes, du plus global au plus fin. Pour classer les cellules immunitaires et stromales nous avons intégré des jeux de données publiques de référence (>275,000 cellules). En parallèle, nous avons développé SCherlock, une méthode pour identifier des marqueurs robustes dans les données scRNAseq, ainsi que fastCNV, une méthode pour estimer la variation du nombre de copies d’ADN (CNVs) dans ces données. Pour les cellules non tumorales, nous avons utilisé des méthodes standard d’intégration des données. Ces méthodes ne sont pas adaptées à l’analyse des cellules tumorales du fait de biais liés à leurs propriétés intrinsèques (cycle cellulaire, CNVs). Par conséquent, nous avons classé les cellules épithéliales tumorales selon leur niveau d’expression des marqueurs des cellules épithéliales normales.RésulatsAu premier niveau, l’analyse non supervisée sépare les cellules épithéliales (tumorales and non tumorales), les cellules lymphoïdes (LB, plasmocytes, LT et NK), les cellules myéloïdes (mastocytes, cellules dendritiques, monocytes, macrophages) et les cellules stromales (entériques gliales, endothéliales et autres cellules mésenchymales). L’analyse des cellules stromales permet d’identifier différents sous-types de fibroblastes d’intérêt. L’analyse des cellules tumorales permet de distinguer les cellules tumorales indifférenciées des cellules tumorales plus différenciées exprimant des marqueurs de différenciation de type caliciforme, entéroendocrine, tuft ou entérocyte. L’atlas ainsi défini a ensuite été appliqué pour déconvoluer des données de transcriptomique spatiale annotées sur le plan histologique, ainsi que les données de RNAseq d’une cohorte de plus de 3000 patients atteints d’un cancer du côlon, annotée cliniquement.ConclusionsNous avons construit un atlas cellulaire repensé du cancer du côlon, et avons pour cela mis au point différentes méthodes et stratégies d’analyse des données scRNAseq. Nos travaux permettent de mieux estimer la composition cellulaire d’un échantillon tumoral, source de potentiels biomarqueurs pronostiques et prédictifs de la réponse au traitement
BackgroundIntra-tumor heterogeneity is a major factor of therapeutic resistance in most cancer types, which in turn motivates numerous efforts to characterize it. Cellular atlases derived from single-cell RNA-seq (scRNAseq) data emerge as a potent way to characterize intra-tumor heterogeneity in terms of cell types and phenotypes. To infer the cellular composition of tumor tissues from their bulk RNA-seq profile, such cellular atlases are used as references by supervised deconvolutions methods. Regarding colon cancers, two cellular atlases were proposed (Pelka 2021; Joanito 2022). They show notable discrepancies regarding their granularity and their tumor cell types. In addition, the proposed tumor cell types appear to be largely irrelevant. Those atlases do not guarantee reliable deconvolution of bulk or spatial RNAseq data, as they were not built for this purpose. Here, we propose a redesigned single-cell atlas of colon cancer cells, constructed to enable the robust deconvolution of colon cancer transcriptomic data, either bulk or spatial. We also present the bioinformatics methods and approaches we developed to build this atlas.MethodsWe combined two scRNAseq published series, totaling over 600,000 cells obtained from the tumors of over 120 colon cancer patients. We randomly derived two subsets from these series, later analyzed independently. We checked that our findings were common to both subsets. We performed an unsupervised clustering analysis independently on each subset, in several steps, from coarse to fine grain. For immune and stromal cells we analyzed data together with four other reference series totaling over 275k cells. We developed SCherlock, a new method for the identification of robust markers of cell types, and fastCNV, a new method to infer Copy Number Variations (CNVs) from scRNA-seq data. We determined non-tumor cell types using standard integration methods. We classified tumor cells in the subspace of normal epithelial cells markers to overcome the biases usually encountered when integrating tumor cells data, in particular those related to CNVs and cell cycle activity.ResultsAt the first step, unsupervised analysis identified separate clusters corresponding to epithelial cells (tumor or non tumor cells), lymphoid cells (B, plasma, T or NK cells), myeloid cells (mastocytes, dendritic cells, monocytes, macrophages), and stromal cells (enteric glial, endothelial and other mesenchymal cells). We independently analyzed these four large cell populations, each with a corresponding reference dataset for non epithelial populations. Known lymphoid and myeloid cell types were identified as expected. Within the stromal population, we identified eight fibroblast subtypes, in addition to pericytes and smooth muscle. Regarding non tumor epithelial cells, we were able to identify enterocytes, goblet cells, BEST4 cells, enteroendocrine cells, Tuft cells, transit amplifying cells, and LGR5 stem cells. Applied to tumor cells, normal epithelial cells expression signatures enabled us to distinguish undifferentiated tumor cells from differentiated tumor cells, expressing markers from either enterocytes, goblet cells, enteroendocrine cells, or Tuft cells. We validated this newly defined cellular atlas of colon cancer cells, through the deconvolution of a set of spatial transcriptomic samples, richly annotated in terms of histological patterns, as well as over through the deconvolution of over 3000 bulk transcriptomic samples. Our classification of both immune and stromal cells, and tumor cells is associated with consensus molecular subtypes and mismatch repair system status.ConclusionsWe built a ready-to-use redesigned cellular atlas of colon cancer, together with new methods and approaches to perform scRNAseq data analysis. Eventually, our tools improve the accuracy of deconvolution methods to infer cellular composition, a source of biomarkers in transcriptomic data to predict prognosis and response to treatment
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Andor, Noemi [Verfasser], Hans-Werner [Akademischer Betreuer] Mewes, Roland R. [Akademischer Betreuer] Rad, and Robert A. J. [Akademischer Betreuer] Oostendorp. "The role of intra-tumoral heterogeneity in the development, progression and recurrence of human malignancies / Noemi Andor. Gutachter: Roland R. Rad ; Hans-Werner Mewes ; Robert A. J. Oostendorp. Betreuer: Hans-Werner Mewes." München : Universitätsbibliothek der TU München, 2014. http://d-nb.info/106537643X/34.

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Book chapters on the topic "Intra-Tumoral heterogeneity"

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Antunes, Jacob, Prateek Prasanna, Anant Madabhushi, Pallavi Tiwari, and Satish Viswanath. "RADIomic Spatial TexturAl descripTor (RADISTAT): Characterizing Intra-tumoral Heterogeneity for Response and Outcome Prediction." In Lecture Notes in Computer Science, 468–76. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-66185-8_53.

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Assadi, Majid, Reza Nemati, Hossein Shooli, and Hojjat Ahmadzadehfar. "Radionuclide Therapy in Brain Tumours." In Beyond Becquerel and Biology to Precision Radiomolecular Oncology: Festschrift in Honor of Richard P. Baum, 109–25. Cham: Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-33533-4_10.

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AbstractGlioblastoma multiforme (GBM), the most common primary brain tumour, is also the most aggressive neoplasm in the brain. It is characterized by a very poor prognosis with a median overall survival time of only 9–15 months. The infiltrating nature of the tumour cells, inter- and intra-tumoral molecular heterogeneity and the tumour’s propensity to hide behind the blood-brain barrier are the key causes of the insufficiency of the optimal available treatments (surgery, radiotherapy and chemotherapy). Furthermore, the best treatment strategy for patients with recurrent GBM remains uncertain and controversial yet. Despite applying state-of-the-art treatments in the majority of patients, the recurrence of the disease is common and the median survival after recurrence is 8.0–9.8 months. In order to avoid treatment insufficiencies, precision medicine-based therapeutics have emerged. An alternative method of treatment is targeted radionuclide therapy, which targets tumour-specified molecules on the surface of tumour cells. It has been shown that brain tumours overexpress several peptides on their surface, which may or may not be immunologically active, that can be used as a biologic target for the therapy. Radionuclide therapy involves the coupling of a peptide, which targets tumour-specific peptides, with a radionuclide payload to selectively irradiate tumour cells with negligible damage to the adjacent healthy tissue. This chapter discusses the use of radiolabelled conjugates for the treatment of brain tumours.
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Gasparini, Giampietro. "Prognostic and predictive value of intra-tumoral microvessel density in human solid tumours." In Tumour Angiogenesis, 29–44. Oxford University PressOxford, 1997. http://dx.doi.org/10.1093/oso/9780198549376.003.0004.

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Abstract The majority of human solid tumours are heterogeneous diseases, made up of multiple cell clones with diverse biological aggressiveness (1,2). The tumour cell heterogeneity is the result of the genomic instability (3) due to genetic alterations that may include: mutations, deletions, chromosomal rearrangements, activation of oncogenes, downregulation of tumour suppressor genes, and gene amplification (4-7). Tumour cell heterogeneity may confer different properties of growth, immunogenicity, ability to metastatize, and sensitivity to treatments to the diverse cell clones. Tumour cell genomic instability and heterogeneity are the biological basis of the clinical observation that both the outcome of the patients and their responsiveness to anti-cancer therapy may be different among tumours classified as having the same pathological or clinical stage. Thus, for several solid tumours it is difficult, by using conventional clinicopathological criteria, to assess the prognosis or the likelihood of response to a specific form of anti-cancer treatment of any single patient.
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Sarma, Kangkan, Dr (Mohd) Habban Akhter, Dr Swati Arya, Monika Dhaka, and Vaishnavi Shinde. "LUNG CANCER: AFFECTED GENE/GENOME, CURRENT TREATMENT PROFILE, AND PROSPECTIVE OF TARGETED DRUG DELIVERY SYSTEM." In Futuristic Trends in Medical Sciences Volume 3 Book 26, 1–44. Iterative International Publisher, Selfypage Developers Pvt Ltd, 2024. http://dx.doi.org/10.58532/v3bfms26p1ch1.

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Lung cancer is the second highest occurrence and lowest survival rate cancer. It is due to its late-stage diagnosis, poor prognosis, and intra-tumoral heterogeneity nature. Further, the drug delivery to the lung is challenging and it affects the treatment effectiveness. They release chemokines and cytokines from the tumor microenvironment (TME). To improve the effectiveness of treatment, researchers emphasize personalized genomic targeting adjuvant therapies along with conventional ones. This study explored the different genomic changes occur due to the prime etiological factors, their reported treatment profile, and nanocarrier roles and strategies to improve the treatment profile’s effectiveness by striving for TME. A biofunctionalized nanocarrier stimulates biosystem interaction, cellular uptake, immune system escape, and vascular changes for penetration into the TME. Inorganic metal compounds scavenge reactive oxygen species (ROS) through their photothermal effect. Stroma, hypoxia, pH, and immunity-modulating agents conjugated or modified nanocarriers co-administered with condition-modulating agents can regulate extracellular matrix (ECM), Cancer-associated fibroblasts (CAF),Tyro3, Axl, and Mertk receptors (TAM) regulation, regulatory T-cell (Treg) inhibition, and myeloid-derived suppressor cells (MDSC) inhibition. Again, biomimetic conjugation or the surface modification of nanocarriers using ligands can enhance active targeting to the genome by bypassing the TME. A carrier system with biofunctionalized inorganic metal compounds and organic compound complex-loaded drugs is convenient for lung-targeted therapy.
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Conference papers on the topic "Intra-Tumoral heterogeneity"

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Diamandis, Phedias. "Abstract IA-03: Unsupervised resolution of intra- and inter-tumoral heterogeneity using deep learning." In Abstracts: AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; January 13-14, 2021. American Association for Cancer Research, 2021. http://dx.doi.org/10.1158/1557-3265.adi21-ia-03.

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Reuben, Alexandre, Zachary A. Cooper, Whijae Roh, Yu Cao, Jacob Austin-Breneman, Hong Jiang, Rodabe N. Amaria, et al. "Abstract 1301: Inter- and intra-tumoral immune and genomic heterogeneity in patients with metastatic melanoma." In Proceedings: AACR 106th Annual Meeting 2015; April 18-22, 2015; Philadelphia, PA. American Association for Cancer Research, 2015. http://dx.doi.org/10.1158/1538-7445.am2015-1301.

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Motomura, Kazuya, Michel Mittelbronn, Werner Paulus, Benjamin Brokinkel, Kathy Keyvani, Ulrich Sure, Karsten Wrede, et al. "Abstract 2406: Intra-tumoral heterogeneity of PDGFRA / MET gain in WHO grade II diffuse astrocytomas." In Proceedings: AACR 104th Annual Meeting 2013; Apr 6-10, 2013; Washington, DC. American Association for Cancer Research, 2013. http://dx.doi.org/10.1158/1538-7445.am2013-2406.

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Wacker, Marcel, Gioele Medici, Marissa Dubbelaar, Jens Bauer, Annika Nelde, Friederike Hanssen, Carolin Schwitalla, et al. "1463 The intra-tumoral spatial heterogeneity of T cell antigens in glioblastoma: An integrated multi-omics approach." In SITC 38th Annual Meeting (SITC 2023) Abstracts. BMJ Publishing Group Ltd, 2023. http://dx.doi.org/10.1136/jitc-2023-sitc2023.1463.

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Varella-Garcia, Marileila, Antonella Flacco, Yong Gon Cho, Aik-Choon Tan, Scott Kono, Wilbur Franklin, Federico Cappuzzo, Lucio Crino, Robert Doebele, and D. Ross Camidge. "Abstract 812: Lack of intra-tumoral heterogeneity in lung adenocarcinoma supports gene fusions involving ALK as early clonal events." In Proceedings: AACR 101st Annual Meeting 2010‐‐ Apr 17‐21, 2010; Washington, DC. American Association for Cancer Research, 2010. http://dx.doi.org/10.1158/1538-7445.am10-812.

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Oikawa, M., A. Igawa, M. Ishida, Y. Nakamura, S. Nishimura, C. Koga, S. Akiyoshi, et al. "Abstract P6-07-10: Cytogenetic analysis of squamous cell carcinoma of the breast reveals inter- and intra-tumoral heterogeneity." In Abstracts: Thirty-Eighth Annual CTRC-AACR San Antonio Breast Cancer Symposium; December 8-12, 2015; San Antonio, TX. American Association for Cancer Research, 2016. http://dx.doi.org/10.1158/1538-7445.sabcs15-p6-07-10.

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Bubie, Adrian, Paula Restrepo, Amanda Craig, Ismail Labgaa, Myron Schwartz, Swan Thung, Gustavo Stolovitzky, Bojan Losic, and Augusto Villanueva. "Abstract 1507: Regional DNA methylation profiling reveals novel epigenetic intra-tumoral heterogeneity signatures and aberrant molecular clocks in hepatocellular carcinoma." In Proceedings: AACR Annual Meeting 2020; April 27-28, 2020 and June 22-24, 2020; Philadelphia, PA. American Association for Cancer Research, 2020. http://dx.doi.org/10.1158/1538-7445.am2020-1507.

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Allison, KH, SM Dintzis, and RA Schmidt. "Abstract P6-05-03: Analysis of College of American Pathologists Recommendations for Reporting HER2 Intra-Tumoral heterogeneity (ITH) on 1329 Breast Cancers." In Abstracts: Thirty-Third Annual CTRC‐AACR San Antonio Breast Cancer Symposium‐‐ Dec 8‐12, 2010; San Antonio, TX. American Association for Cancer Research, 2010. http://dx.doi.org/10.1158/0008-5472.sabcs10-p6-05-03.

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Schmidt, RA, SM Dintzis, and KH Allison. "Abstract P6-05-04: Quantification of Intra-Tumoral Heterogeneity for HER2 Gene Amplification by FISH with Proposed New (“High-ICR”) Reporting System." In Abstracts: Thirty-Third Annual CTRC‐AACR San Antonio Breast Cancer Symposium‐‐ Dec 8‐12, 2010; San Antonio, TX. American Association for Cancer Research, 2010. http://dx.doi.org/10.1158/0008-5472.sabcs10-p6-05-04.

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Feijtel, D., G. Doeswijk, J. Haeck, M. Clahsen-van Groningen, M. Konijnenberg, D. Van Gent, M. Hendriks-de Jong, and J. Nonnekens. "PO-132 Dissecting the radiobiology of targeted radionuclide therapy reveals an intra-tumoral heterogeneic response in a preclinicalin vivomodel." In Abstracts of the 25th Biennial Congress of the European Association for Cancer Research, Amsterdam, The Netherlands, 30 June – 3 July 2018. BMJ Publishing Group Ltd, 2018. http://dx.doi.org/10.1136/esmoopen-2018-eacr25.656.

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