Добірка наукової літератури з теми "Biopsy-based transcriptomics"

Abstract Background Colonoscopy is the gold standard for evaluation of inflammation in inflammatory bowel diseases (IBD), yet entails cumbersome preparations and risks of injury. Existing non-invasive prognostic tools are limited in their diagnostic power. Moreover, transcriptomics of colonic biopsies have been inconclusive in their association with clinical features. Our aim was to assess the utility of host transcriptomics of fecal wash samples of IBD patients compared to controls. Methods In this prospective cohort study, we obtained biopsies and fecal-wash samples from IBD patients and controls undergoing lower endoscopy. We performed RNAseq of biopsies and matching fecal-washes, and associated them with endoscopic and histological inflammation status. We also performed fecal mass-spectrometry proteomics on a subset of samples. We inferred cell compositions using computational deconvolution and used classification algorithms to identify informative genes. Results We analyzed biopsies and fecal washes from 39 patients (19 IBD, 20 controls). Host fecal-transcriptome carried information that was distinct from biopsy RNAseq and fecal proteomics. Transcriptomics of fecal washes, yet not of biopsies, from patients with histological inflammation were significantly correlated to one another (p=5.3*10–12). Fecal-transcriptome was significantly more powerful in identifying histological inflammation compared to transcriptome of intestinal biopsies (150 genes with area-under-the-curve >0.9 in fecal samples versus 10 genes in biopsy RNAseq). Fecal samples were enriched in inflammatory monocytes, regulatory T cells, natural killer-cells and innate lymphoid cells. Figure 1 - Fecal-wash host transcriptome predicts histological inflammation. A) Study layout, B) Clustergram of fecal-wash host cell mRNA signatures, demonstrating that patients with histological inflammation (red) are clustered when measuring fecal wash transcriptome yet not biopsy transcriptomes. C-D) Principle Component Analysis demonstrating improved separation of inflamed patients based on fecal host transcriptomes. E, F) Expression of host genes in fecal washes has higher statistical power (Area under the Curve, AUC) in classifying histological inflammation compared to biopsies. D shows NFKBIA as an example, E shows the AUC of the 5% best classifying genes, F shows the overall AUC based on biopsies or washes. Gray areas have AUC>0.9. G) UMAP of cells obtained from scRNAseq of mouse small intestine fecal washes. Conclusion Fecal wash host transcriptome is a powerful biomarker reflecting histological inflammation. Furthermore, it opens the way to identifying important correlates and therapeutic targets that may be obscure using biopsy transctriptomics.

Abstract Background Non-invasive modalities for assessing active endoscopic and histological inflammation in Crohn’s disease and Ulcerative Colitis patients are critically needed. Fecal wash host transcriptomics has been shown to be a robust classifier of endoscopic and histological inflammation in inflammatory bowel disease (IBD) patients with distal colitis. Whether such fecal washes can inform on inflammatory processes occurring in more proximal intestinal segments is currently unknown. Methods Crohn's disease and Ulcerative Colitis patients as well as healthy controls were prospectively enrolled. Host transcriptomes of biopsies and fecal washes obtained during colonoscopy at predefined locations throughout the colon and terminal ileum were analyzed and associated with same time-point clinical, endoscopic and histological parameters. Results 59 IBD patients and 50 controls were prospectively enrolled. Biospies and fecal washes from the distal, proximal colon and terminal ileum were compared. We find that host transcriptomics of distal fecal washes robustly classify histological inflammation in ileal and proximal colonic Crohn’s disease, even without distal colonic involvement. Receiver operating characteristic (ROC) curve analysis demonstrated significantly higher classification power based on the fecal wash transcriptomics compared to biopsies: Area Under the Curve, AUC=0.94+-0.07 for UC distal fecal washes and AUC=0.94+-0.09 for distal fecal washes of CD patients without distal colonic involvement, as compared to all distal biopsies AUC= 0.82+-0.20. We further demonstrate that fecal washes consist of modules of co-expressed immune, stromal and epithelial genes. Fecal wash expression of gene modules previously associated with lack of response to biological therapies correlates with disease severity. Figure 1 - Host transcriptomics of fecal washes from different intestinal segments captures information that is distinct from same – segment biopsy transcriptomics. A) Experimental layout. B) Clustergram of transcriptomes of biopsies (purple) and fecal washes (yellow). C) Principal component analysis (PCA) of biopsies (purple) and fecal washes (yellow). Black circles highlight inflamed samples. D) Spearman correlations between the transcriptomes of pairs of either biopsies or fecal washes that are both annotated as inflamed and mixed pairs (one annotated as inflamed and the other not). (A) Created with BioRender.com. TI – terminal ileum, P – proximal colon, D – distal colon. Conclusion Our study establishes the power of colonic fecal washes for identifying pathological processes throughout the ileal-colonic axis.

Several studies have employed DNA microarrays to identify gene expression signatures that mark human ageing; yet the features underlying this complicated phenomenon remain elusive. We thus conducted a bioinformatics meta-analysis on transcriptomics data from human cell- and biopsy-based microarrays experiments studying cellular senescence orin vivotissue ageing, respectively. We report that coregulated genes in the postmitotic muscle and nervous tissues are classified into pathways involved in cancer, focal adhesion, actin cytoskeleton, MAPK signalling, and metabolism regulation. Genes that are differentially regulated during cellular senescence refer to pathways involved in neurodegeneration, focal adhesion, actin cytoskeleton, proteasome, cell cycle, DNA replication, and oxidative phosphorylation. Finally, we revealed genes and pathways (referring to cancer, Huntington’s disease, MAPK signalling, focal adhesion, actin cytoskeleton, oxidative phosphorylation, and metabolic signalling) that are coregulated during cellular senescence andin vivotissue ageing. The molecular commonalities between cellular senescence and tissue ageing are also highlighted by the fact that pathways that were overrepresented exclusively in the biopsy- or cell-based datasets are modules either of the same reference pathway (e.g., metabolism) or of closely interrelated pathways (e.g., thyroid cancer and melanoma). Our reported meta-analysis has revealed novel age-related genes, setting thus the basis for more detailed future functional studies.

Prostate cancer (PCa) is a highly prevalent cancer type with a heterogeneous prognosis. An accurate assessment of tumor aggressiveness can pave the way for tailored treatment strategies, potentially leading to better outcomes. While tumor aggressiveness is typically assessed based on invasive methods (e.g., biopsy), radiogenomics, combining diagnostic imaging with genomic information can help uncover aggressive (imaging) phenotypes, which in turn can provide non-invasive advice on individualized treatment regimens. In this study, we carried out a parallel analysis on both imaging and transcriptomics data in order to identify features associated with clinically significant PCa (defined as an ISUP grade ≥ 3), subsequently evaluating the correlation between them. Textural imaging features were extracted from multi-parametric MRI sequences (T2W, DWI, and DCE) and combined with DCE-derived parametric pharmacokinetic maps obtained using magnetic resonance dispersion imaging (MRDI). A transcriptomic analysis was performed to derive functional features on transcription factors (TFs), and pathway activity from RNA sequencing data, here referred to as transcriptomic features. For both the imaging and transcriptomic features, different machine learning models were separately trained and optimized to classify tumors in either clinically insignificant or significant PCa. These models were validated in an independent cohort and model performance was used to isolate a subset of relevant imaging and transcriptomic features to be further investigated. A final set of 31 imaging features was correlated to 33 transcriptomic features obtained on the same tumors. Five significant correlations (p < 0.05) were found, of which, three had moderate strength (|r| ≥ 0.5). The strongest significant correlations were seen between a perfusion-based imaging feature—MRDI A median—and the activities of the TFs STAT6 (−0.64) and TFAP2A (−0.50). A higher-order T2W textural feature was also significantly correlated to the activity of the TF STAT6 (−0.58). STAT6 plays an important role in controlling cell proliferation and migration. Loss of the AP2alpha protein expression, quantified by TFAP2A, has been strongly associated with aggressiveness and progression in PCa. According to our findings, a combination of texture features extracted from T2W and DCE, as well as perfusion-based pharmacokinetic features, can be considered for the prediction of clinically significant PCa, with the pharmacokinetic MRDI A feature being the most correlated with the underlying transcriptomic information. These results highlight a link between quantitative imaging features and the underlying transcriptomic landscape of prostate tumors.

Abstract Introduction and aims Keratosis linearis with ichthyosis congenita and sclerosing keratoderma (KLICK) syndrome is an ultrarare genodermatosis associated with a homozygous pathogenic variant, c.-95delC in POMP, which encodes for proteasome maturation protein. Clinical manifestations include linear hyperkeratotic papules in the flexures, palmoplantar keratoderma and pseudoainhum. The pathophysiology of KLICK syndrome is poorly understood, resulting in nonspecific, limited treatment options. The aim of this work is to characterize KLICK syndrome at the cellular and molecular level, with the utilization of transcriptomic techniques for potential drug repurposing. Methods A lesional skin biopsy was obtained, following informed consent, from a 32-year-old female patient with KLICK syndrome. Histology and immunostaining were used to characterize the disrupted skin architecture. RNA sequencing was used to identify key dysregulated pathways and the L1000FWD reverse transcriptomics tool enabled the generation of a shortlist of potential therapeutics for KLICK syndrome. CRISPR editing techniques and induced pluripotent stem cell (iPSC) reprogramming Methods were used to generate cellular based models carrying the pathogenic POMP variant in order to support evaluation of the shortlisted therapeutic options. Results KLICK syndrome skin histology featured hyperkeratosis, hypergranulosis and delayed cellular flattening. Consistent with pathological features, pathway analysis of transcriptomic data revealed keratinocyte differentiation and epidermis development as key upregulated pathways in KLICK syndrome. Interestingly, the Panther pathway analysis tool suggested dysregulation in epidermal growth factor receptor (EGFR) signalling, and reverse transcriptomics identified erlotinib, an EGFR inhibitor, as a potential therapeutic treatment for KLICK syndrome. HaCaT keratinocytes were successfully edited using CRISPR-Cas9 to carry the KLICK patient POMP variant and a KLICK patient iPSC line was generated to support in vitro KLICK syndrome modelling. Conclusions KLICK syndrome is a hyperproliferative inflammatory skin disorder associated with a pathogenic variant in POMP. Reverse transcriptomics is a useful tool for shortlisting drugs with biologically relevant mechanisms of action with potential for treatment of rare genetic diseases such as KLICK syndrome.

Abstract Breast cancer is a leading cause of cancer-related mortality for women worldwide. Hormone receptor-positive (HR+) breast tumors, which represent 70% of all breast cancer cases, are treated with endocrine therapy. However, not all patients benefit from this treatment regimen because of patient-to-patient variation. Therefore, high throughput drug screening system is warranted to enable personalized medicine. Patient-derived organoids, which serve as a screening platform, lose their hormone receptors and response upon ex vivo culturing, and may not be adequate models for HR+ breast tumors. Moreover, unidentified biological components in the widely-used basement membrane matrix, Matrigel, result in high batch-to-batch variations and poor reproducibility in organoid cultures. Here, we propose a hydrogel-based 3D ex vivo model with defined structural and chemical properties to test hormone and drug sensitivity of HR+ breast tumors from patient-derived xenografts (PDXs) and patient tumor biopsies using microfluidics. Our data demonstrate the feasibility of this model to preserve cell proliferation and hormone receptor expression over 7 days. We also demonstrate that responses to hormones and FDA-approved drugs are faithfully maintained in this model. Finally, to establish a high throughput hormone and drug testing workflow with transcriptomic readout, we multiplexed barcoded- and drug-treated tumor samples in a single experiment with bulk RNA-sequencing. Our preliminary data demonstrate patient-specific responses to hormones and drugs that correspond to patient genetic mutation profiles, treatment history, disease stages and subtypes. This platform also enables testing of drugs in clinical trials that shows promising therapeutic outcomes for breast cancer, such as CDK4/6i, AKTi, PARPi, mTORi and their combination with endocrine therapy, thanks to the high throughput of the screening system and the low consumption of patient-derived or patient tissue. Given the capability of combining this physiologically-relevant 3D ex vivo model with RNA-seq for HR+ breast tumors, this platform holds potential for high throughput compound testing and transcriptomic profiling of patient biopsies for personalized medicine. Citation Format: Yueyun Zhang, Carlos Henrique Venturi Ronchi, Giovanna Ambrosini, Yuanlong Liu, Patrick Aouad, Daria Matvienko, Christoph Merten, Cathrin Brisken. Transcriptomics-based drug screening in 3D ex vivo patient-drived breast cancer model and patient biopsy for personalized therapy [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-14-06.

: Liquid biopsy, based on the circulating tumor cells (CTCs) and cell-free nucleic acids has potential applications at multiple points throughout the natural course of cancer, from diagnosis to follow-up. The advantages of doing ctDNA assessment vs. tissue-based genomic profile are the minimal procedural risk, the possibility to serial testing in order to monitor disease-relapse and response to therapy over time and to reduce hospitalization costs during the entire process. However, some critical issues related to ctDNA assays should be taken into consideration. The sensitivity of ctDNA assays depends on the assessment technique and genetic platforms used, on tumor-organ, stage, tumor heterogeneity, tumor clonality. The specificity is usually very high, whereas the concordance with tumor-based biopsy is generally low. In patients with renal cell carcinoma (RCC), qualitative analyses of ctDNA have been performed with interesting results regarding selective pressure from therapy, therapeutic resistance, exceptional treatment response to everolimus and mutations associated with aggressive behavior. Quantitative analyses showed variations of ccfDNA levels at different tumor stage. Compared to CTC assay, ctDNA is more stable than cells and easier to isolate. Splice variants, information at single-cell level and functional assays along with proteomics, transcriptomics and metabolomics studies can be performed only in CTCs.

Biomarkers are vital in healthcare as they provide valuable insights into disease diagnosis, prognosis, treatment response, and personalized medicine. They serve as objective indicators, enabling early detection and intervention, leading to improved patient outcomes and reduced costs. Biomarkers also guide treatment decisions by predicting disease outcomes and facilitating individualized treatment plans. They play a role in monitoring disease progression, adjusting treatments, and detecting early signs of recurrence. Furthermore, biomarkers enhance drug development and clinical trials by identifying suitable patients and accelerating the approval process. In this review paper, we described a variety of biomarkers applicable for cancer detection and diagnosis, such as imaging-based diagnosis (CT, SPECT, MRI, and PET), blood-based biomarkers (proteins, genes, mRNA, and peptides), cell imaging-based diagnosis (needle biopsy and CTC), tissue imaging-based diagnosis (IHC), and genetic-based biomarkers (RNAseq, scRNAseq, and spatial transcriptomics).

Objectives: Follicular lymphoma (FL) typically originates from premalignant mature B cells that carry the founder t(14;18) BCL2 translocation. Mutations in epigenetic modifiers and acquisition of N-glycosylation sites in CDR regions of the B-cell receptor (BCR) are recurrent secondary events in FL pathogenesis. Despite these oncogenic drivers, FL can remain indolent and clinically stable for years. The molecular events driving subclonal evolution into symptomatic progression and eventual transformation to aggressive lymphoma are insufficiently understood. FL cells are frozen in their B-cell development at the germinal center stage and undergo continuous somatic hypermutation mediated by expression of activation-induced deaminase (AID). We aim to identify crucial drivers of subclonal FL evolution by high-throughput mapping at single-cell resolution. Methods: Viable FL cells were isolated and cryopreserved from 23 histologically or immunocytologically confirmed FL samples from 13 patients with informed consent. Full-length VDJ/VJ transcripts were isolated by unbiased template-switching ARTISAN PCR and massive parallel NGS sequencing on the PacBio platform. The clonal primordial FL BCR (pBCR) was reconstructed from unmutated IGV/IGJ sequences with the CDR3 of the least mutated BCR. Since the IgTree program was unable to process the obtained numbers of BCR sequences, we developed the WILLOW algorithm for analysis of BCR evolution based on the principle of maximum parsimony and on distance from the pBCR. Intraclonal BCR variability was quantified by Shannon's diversity index. 5' single cell transcriptomics and VDJ/VJ sequencing was performed on 2 pools of highly purified FL cells from 5 lymph node biopsies on the 10x Genomics platform. Data were deconvoluted based on expressed variants by the Single Cell Sample Matcher (SCSM) algorithm. Clustering based on gene expression profiles was performed by shared nearest neighbour (SNN) modularity optimization within the R Seurat package. Genes whose expression differed significantly (adjusted p&lt;0.05) between clusters were assigned to gene ontology terms. Results: ARTISAN PCR/PacBio NGS yielded a median of 743 full-length VDJ and VJ sequences (range 62-12782) per BCR chain with expected high intraclonal diversity (median 200 subclones, range 15-3301). WILLOW revealed dominant FL subclones with a subclonal hierarchy wherein multiple routes converged to offspring nodes with identical additional mutations rather than tree-like branching (Figure). In serial samples of 4 patients, lymph node biopsies had only marginally higher subclonal diversity than blood or bone marrow samples (p=0,055; Wilcoxon's matched-pairs signed rank test). Overall BCR mutational burden increased over time in sequential biopsies. Two cases of histological FL transformation were dominated by a single subclone (65% and 80% of all VDJ/VJ sequences, respectively) that was rare in the preceding FL BCR network (0.2% and 1.8%). Pooled transcriptomics data from 6050-6500 cells were assigned to individual samples by SCSM and revealed up to seven transcriptional clusters per FL. In 9 of 10 FL, genes assigned to immune function strongly contributed to separation into one or more clusters. Single cell VDJ/VJ sequencing yielded combined heavy and light chain BCR sequences for a median of 502 FL cells per biopsy (range 22 - 1919) that permitted mapping of subclonal evolution by WILLOW based on complete BCR information. Transcriptome clusters were not distributed evenly throughout the WILLOW FL BCR networks but rather statistically associated with distinct major FL subclones. Vice versa, major FL subclones within the same biopsy were distinguished by particular gene expression profiles. Conclusions: WILLOW facilitates mapping of subclonal FL evolution based on high-throughput BCR sequencing. FL evolution proceeds in networks rather than tree-like branching, whereby acquisition of certain combinations of several BCR mutations can occur in parallel in different trajectories. Transcriptomic profiling of single FL cells identifies distinct clusters within a single biopsy. Mapping of these clusters to the FL cell position in the subclonal FL evolutionary network identifies putative mechanisms that are associated with subclonal progression. These mechanisms involve physiological B-cell signalling pathways. Figure Disclosures No relevant conflicts of interest to declare.

La transplantation rénale est le meilleur traitement de l’insuffisance rénale chronique terminale, que ce soit en termes d’espérance ou de qualité de vie. Malgré les progrès réalisés en immunologie de la transplantation et dans la gestion globale des patients transplantés, la principale étiologie de perte de greffon reste le rejet, et en particulier le rejet humoral.L’évaluation du risque de rejet humoral repose principalement sur le dosage des anticorps anti-HLA dirigés contre le greffon. Pourtant, il apparait que ces anticorps ont un faible pouvoir prédictif de l’incidence et du pronostic du rejet, ce qui pourrait être expliqué par une hétérogénéité de leurs caractéristiques intrinsèques. Ces caractéristiques dépendent des cellules responsables de leur sécrétion, plasmocytes à courte et longue durée de vie, et donc indirectement des cellules responsables du maintien du pool de ces cellules sécrétrices d’anticorps : les lymphocytes B mémoires. Il a été montré en pathologies infectieuses que ces lymphocytes B mémoires sont hétérogènes en termes de phénotype, de fonction, de degré de clonalité et de diversification de leur BCR (B-cell receptor). Néanmoins, ceci n’a pas encore été analysé en transplantation rénale. Un des objectifs de cette thèse était d’étudier le degré d’hétérogénéité des lymphocytes B mémoires HLA-spécifiques chez des patients immunisés en attente de transplantation rénale. Pour ce faire, une analyse en cellule unique de lymphocytes B mémoires HLA-spécifiques de patients présentant différents contextes et degré d’immunisation a été réalisée, dans le but d’identifier leurs caractéristiques phénotypiques et transcriptomiques et la diversification de leur répertoire BCR.La deuxième partie des travaux s’est concentrée sur les modalités diagnostiques du rejet de greffe rénale. Depuis quelques années, des outils de biologie moléculaire sont disponibles, permettant d’évaluer des centaines de transcrits exprimés dans le tissu de biopsie. Ces outils donnent la possibilité de décrire de nouvelles voies physiopathologiques, et potentiellement d’améliorer le diagnostic du rejet, en particulier le rejet humoral. Néanmoins, leur utilisation en pratique courante est restreinte du fait de leur faible disponibilité, des difficultés à interpréter les données produites, et de leur coût. De plus, du fait de cette sous-utilisation en pratique clinique, leur impact exact dans la prise en charge des patients n’est pas déterminé. Au cours de cette thèse, un outil de diagnostic moléculaire ayant des caractéristiques compatibles avec une utilisation en pratique clinique a été développé. Celui-ci permet de diagnostiquer le rejet et de le classer en rejet humoral ou cellulaire. Dans un second temps, cet outil a été confronté à des situations cliniques litigieuses, afin d’évaluer son intérêt en pratique courante.À travers ces travaux, cette thèse vise d’une part à améliorer la compréhension de la réponse humorale en transplantation rénale, afin de contribuer à terme à mieux stratifier le risque immunologique en transplantation, et d’autre part à améliorer les modalités diagnostiques du rejet en aidant à la généralisation des outils de biologie moléculaire appliqués aux biopsies de greffons rénaux
Kidney transplantation is the best treatment of end-stage renal disease, improving life quality and quantity. Despite advances in pathophysiological knowledge of kidney transplant immunology, kidney transplant rejection remains the major cause of allograft dysfunction, especially antibody-mediated rejection.Antibody-mediated rejection risk assessment is based on the evaluation of donor-specific anti-HLA antibodies. However, these antibodies have a poor predictive value for incidence and prognosis of rejection. This could be explained by the heterogeneity of their intrinsic characteristics. These characteristics depend on cells responsible for their secretion, which include short- and long- lived plasma cells. Consequently, they indirectly depend on the cells responsible for maintaining the pool of these antibody-secreting cells, such as memory B cells. In infectious diseases, it is known that memory B cells are heterogeneous in terms of phenotype, function, degree of clonality, and diversification of their B-cell receptor (BCR). However, this heterogeneity has not been examined in the context of kidney transplantation.The aim of the first part of this thesis was to study the heterogeneity of HLA-specific memory B cells in sensitised patients on kidney transplant waiting list. To this end, single-cell analysis of HLA-specific memory B cells from patients with various aetiologies and degrees of immunisation was performed. This led to their phenotypic and transcriptomic characterisation and to the assessment of their BCR repertoire.The second part of this thesis was dedicated to the diagnosis of kidney transplant rejection.In recent years, biopsy-based transcriptomics has emerged, enabling the assessment of hundreds of transcripts in kidney biopsy tissue. These tools provide the opportunity to elucidate new physiopathological pathways and potentially enhance the diagnosis of rejection, especially humoral rejection. However, their application in clinical practice is still limited due to their restricted availability, required expertise for data processing and interpretation, and cost. Furthermore, their exact impact on patient management remains undetermined. Here, a molecular diagnostic tool with characteristics suitable for clinical use was developed. This tool enables the diagnosis of rejection and its classification between antibody-mediated and T-cell mediated rejection. Subsequently, this tool was assessed in ambiguous clinical situations to evaluate its impact in clinical practice.Through these studies, this thesis focused on enhancing our understanding of the humoral response in renal transplantation, which could help improving immunological risk stratification in transplantation. Additionally, it aimed to improve biopsy-based transcriptomics in the diagnosis of kidney transplant rejection