RESUMEN
Despite the well-established significance of transcription factors (TFs) in pathogenesis, their utilization as pharmacological targets has been limited by the inherent challenges in modulating their protein interactions. The lack of defined small-molecule binding pockets and the nuclear localization of TFs do not favor the use of traditional tools. Aptamers possess large molecular weights, expansive blocking surfaces and efficient cellular internalization, making them compelling tools for modulating TF interactions. Here, we report a structure-guided design strategy called Blocker-SELEX to develop inhibitory aptamers (iAptamers) that selectively block TF interactions. Our approach leads to the discovery of iAptamers that cooperatively disrupt SCAF4/SCAF8-RNAP2 interactions, dysregulating RNAP2-dependent gene expression, which impairs cell proliferation. This approach is further applied to develop iAptamers blocking WDR5-MYC interactions. Overall, our study highlights the potential of iAptamers in disrupting pathogenic TF interactions, implicating their potential utility in studying the biological functions of TF interactions and in nucleic acids drug discovery.
Asunto(s)
Aptámeros de Nucleótidos , Técnica SELEX de Producción de Aptámeros , Factores de Transcripción , Aptámeros de Nucleótidos/farmacología , Aptámeros de Nucleótidos/química , Aptámeros de Nucleótidos/metabolismo , Humanos , Factores de Transcripción/metabolismo , Unión Proteica , Proliferación Celular/efectos de los fármacos , ARN Polimerasa II/metabolismo , Células HEK293 , Proteínas Proto-Oncogénicas c-myc/metabolismo , Proteínas Proto-Oncogénicas c-myc/genética , Proteínas Proto-Oncogénicas c-myc/antagonistas & inhibidoresRESUMEN
The process of drug discovery is widely known to be lengthy and resource-intensive. Artificial Intelligence approaches bring hope for accelerating the identification of molecules with the necessary properties for drug development. Drug-likeness assessment is crucial for the virtual screening of candidate drugs. However, traditional methods like Quantitative Estimation of Drug-likeness (QED) struggle to distinguish between drug and non-drug molecules accurately. Additionally, some deep learning-based binary classification models heavily rely on selecting training negative sets. To address these challenges, we introduce a novel unsupervised learning framework called DrugMetric, an innovative framework for quantitatively assessing drug-likeness based on the chemical space distance. DrugMetric blends the powerful learning ability of variational autoencoders with the discriminative ability of the Gaussian Mixture Model. This synergy enables DrugMetric to identify significant differences in drug-likeness across different datasets effectively. Moreover, DrugMetric incorporates principles of ensemble learning to enhance its predictive capabilities. Upon testing over a variety of tasks and datasets, DrugMetric consistently showcases superior scoring and classification performance. It excels in quantifying drug-likeness and accurately distinguishing candidate drugs from non-drugs, surpassing traditional methods including QED. This work highlights DrugMetric as a practical tool for drug-likeness scoring, facilitating the acceleration of virtual drug screening, and has potential applications in other biochemical fields.
Asunto(s)
Descubrimiento de Drogas , Descubrimiento de Drogas/métodos , Preparaciones Farmacéuticas/química , Preparaciones Farmacéuticas/clasificación , Algoritmos , Aprendizaje Profundo , Inteligencia ArtificialRESUMEN
Although substantial efforts have been made using graph neural networks (GNNs) for artificial intelligence (AI)-driven drug discovery, effective molecular representation learning remains an open challenge, especially in the case of insufficient labeled molecules. Recent studies suggest that big GNN models pre-trained by self-supervised learning on unlabeled datasets enable better transfer performance in downstream molecular property prediction tasks. However, the approaches in these studies require multiple complex self-supervised tasks and large-scale datasets , which are time-consuming, computationally expensive and difficult to pre-train end-to-end. Here, we design a simple yet effective self-supervised strategy to simultaneously learn local and global information about molecules, and further propose a novel bi-branch masked graph transformer autoencoder (BatmanNet) to learn molecular representations. BatmanNet features two tailored complementary and asymmetric graph autoencoders to reconstruct the missing nodes and edges, respectively, from a masked molecular graph. With this design, BatmanNet can effectively capture the underlying structure and semantic information of molecules, thus improving the performance of molecular representation. BatmanNet achieves state-of-the-art results for multiple drug discovery tasks, including molecular properties prediction, drug-drug interaction and drug-target interaction, on 13 benchmark datasets, demonstrating its great potential and superiority in molecular representation learning.
Asunto(s)
Inteligencia Artificial , Benchmarking , Sistemas de Liberación de Medicamentos , Descubrimiento de Drogas , Redes Neurales de la ComputaciónRESUMEN
Clinical trials frequently include multiple end points that mature at different times. The initial report, typically based on the primary end point, may be published when key planned co-primary or secondary analyses are not yet available. Clinical Trial Updates provide an opportunity to disseminate additional results from studies, published in JCO or elsewhere, for which the primary end point has already been reported.The REMoDL-B phase III adaptive trial compared rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisolone (R-CHOP) versus R-CHOP + bortezomib (RB-CHOP) in patients with diffuse large B-cell lymphoma (DLBCL), stratified by molecular subtype. Primary analysis at a median follow-up of 30 months found no effect of bortezomib on progression-free survival (PFS) or overall survival (OS). Retrospective analysis using a gene expression-based classifier identified a molecular high-grade (MHG) group with worse outcomes. We present an updated analysis for patients successfully classified by the gene expression profile (GEP). Eligible patients were age older than 18 years with untreated DLBCL, fit enough for full-dose chemotherapy, and with adequate biopsies for GEP. Of 1,077 patients registered, 801 were identified with Activated B-Cell (ABC), Germinal Center B-cell, or MHG lymphoma. At a median follow-up of 64 months, there was no overall benefit of bortezomib on PFS or OS (5-year PFS hazard ratio [HR], 0.81; P = .085; OS HR, 0.86; P = .32). However, improved PFS and OS were seen in ABC lymphomas after RB-CHOP: 5-year OS 67% with R-CHOP versus 80% with RB-CHOP (HR, 0.58; 95% CI, 0.35 to 0.95; P = .032). Five-year PFS was higher in MHG lymphomas: 29% versus 55% (HR, 0.46; 95% CI, 0.26 to 0.84). Patients with ABC and MHG DLBCL may benefit from the addition of bortezomib to R-CHOP in initial therapy.
Asunto(s)
Linfoma de Células B Grandes Difuso , Adolescente , Humanos , Anticuerpos Monoclonales de Origen Murino/uso terapéutico , Protocolos de Quimioterapia Combinada Antineoplásica/efectos adversos , Bortezomib , Ciclofosfamida , Doxorrubicina , Estudios de Seguimiento , Linfoma de Células B Grandes Difuso/tratamiento farmacológico , Linfoma de Células B Grandes Difuso/genética , Linfoma de Células B Grandes Difuso/patología , Prednisona , Estudios Retrospectivos , Rituximab , VincristinaRESUMEN
Despite the effectiveness of immuno-chemotherapy, 40% of patients with diffuse large B-cell lymphoma (DLBCL) experience relapse or refractory disease. Longitudinal studies have previously focused on the mutational landscape of relapse but fell short of providing a consistent relapse-specific genetic signature. In our study, we have focused attention on the changes in GEP accompanying DLBCL relapse using archival paired diagnostic/relapse specimens from 38 de novo patients with DLBCL. COO remained stable from diagnosis to relapse in 80% of patients, with only a single patient showing COO switching from activated B-cell-like (ABC) to germinal center B-cell-like (GCB). Analysis of the transcriptomic changes that occur following relapse suggest ABC and GCB relapses are mediated via different mechanisms. We developed a 30-gene discriminator for ABC-DLBCLs derived from relapse-associated genes that defined clinically distinct high- and low-risk subgroups in ABC-DLBCLs at diagnosis in datasets comprising both population-based and clinical trial cohorts. This signature also identified a population of <60-year-old patients with superior PFS and OS treated with ibrutinib-R-CHOP as part of the PHOENIX trial. Altogether this new signature adds to the existing toolkit of putative genetic predictors now available in DLBCL that can be readily assessed as part of prospective clinical trials.
Asunto(s)
Linfoma de Células B Grandes Difuso , Recurrencia Local de Neoplasia , Humanos , Persona de Mediana Edad , Estudios Prospectivos , Linfoma de Células B Grandes Difuso/diagnóstico , Linfoma de Células B Grandes Difuso/tratamiento farmacológico , Linfoma de Células B Grandes Difuso/genética , Linfocitos B/metabolismo , Centro Germinal/metabolismoRESUMEN
Cell-of-origin subclassification of diffuse large B cell lymphoma (DLBCL) into activated B cell-like (ABC), germinal centre B cell-like (GCB) and unclassified (UNC) or type III by gene expression profiling is recommended in the latest update of the World Health Organization's classification of lymphoid neoplasms. There is, however, no accepted gold standard method or dataset for this classification. Here, we compare classification results using gene expression data for 68 formalin-fixed paraffin-embedded DLBCL samples measured on four different gene expression platforms (Illumina wG-DASLTM arrays, Affymetrix PrimeView arrays, Illumina TrueSeq RNA sequencing and the HTG EdgeSeq DLBCL Cell of Origin Assay EU using an established platform agnostic classification algorithm (DAC) and the classifier native to the HTG platform, which is CE marked for in vitro diagnostic use (CE-IVD). Classification methods and platforms show a high level of concordance, with agreement in at least 80% of cases and rising to much higher levels for classifications of high confidence. Our results demonstrate that cell-of-origin classification by gene expression profiling on different platforms is robust, and that the use of the confidence value alongside the classification result is important in clinical applications.
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Perfilación de la Expresión Génica , Linfoma de Células B Grandes Difuso/genética , Regulación Neoplásica de la Expresión Génica , Secuenciación de Nucleótidos de Alto Rendimiento , Humanos , Linfoma de Células B Grandes Difuso/clasificación , Análisis de Secuencia por Matrices de Oligonucleótidos , ARN/genética , TranscriptomaRESUMEN
Using a Burkitt lymphoma-like gene expression signature, we recently defined a high-risk molecular high-grade (MHG) group mainly within germinal centre B-cell like diffuse large B-cell lymphomas (GCB-DLBCL), which was enriched for MYC/BCL2 double-hit (MYC/BCL2-DH). The genetic basis underlying MHG-DLBCL and their aggressive clinical behaviour remain unknown. We investigated 697 cases of DLBCL, particularly those with MYC/BCL2-DH (n = 62) by targeted sequencing and gene expression profiling. We showed that DLBCL with MYC/BCL2-DH, and those with BCL2 translocation, harbour the characteristic mutation signatures that are associated with follicular lymphoma and its high-grade transformation. We identified frequent MYC hotspot mutations that affect the phosphorylation site (T58) and its adjacent amino acids, which are important for MYC protein degradation. These MYC mutations were seen in a subset of cases with MYC translocation, but predominantly in those of MHG. The mutations were more frequent in double-hit lymphomas with IG as the MYC translocation partner, and were associated with higher MYC protein expression and poor patient survival. DLBCL with MYC/BCL2-DH and those with BCL2 translocation alone are most likely derived from follicular lymphoma or its precursor lesion, and acquisition of MYC pathogenic mutations may augment MYC function, resulting in aggressive clinical behaviour.
Asunto(s)
Biomarcadores de Tumor/genética , Evolución Clonal , Linfoma de Células B Grandes Difuso/genética , Mutación , Proteínas Proto-Oncogénicas c-bcl-2/genética , Proteínas Proto-Oncogénicas c-myc/genética , Femenino , Perfilación de la Expresión Génica , Regulación Leucémica de la Expresión Génica , Reordenamiento Génico , Humanos , Linfoma de Células B Grandes Difuso/patología , Masculino , Persona de Mediana Edad , Fenotipo , Pronóstico , Tasa de Supervivencia , Translocación GenéticaRESUMEN
BACKGROUND: Biologically distinct subtypes of diffuse large B-cell lymphoma can be identified using gene-expression analysis to determine their cell of origin, corresponding to germinal centre or activated B cell. We aimed to investigate whether adding bortezomib to standard therapy could improve outcomes in patients with these subtypes. METHODS: In a randomised evaluation of molecular guided therapy for diffuse large B-cell lymphoma with bortezomib (REMoDL-B), an open-label, adaptive, randomised controlled, phase 3 superiority trial, participants were recruited from 107 cancer centres in the UK (n=94) and Switzerland (n=13). Eligible patients had previously untreated, histologically confirmed diffuse large B-cell lymphoma with sufficient diagnostic material from initial biopsies for gene-expression profiling and pathology review; were aged 18 years or older; had ECOG performance status of 2 or less; had bulky stage I or stage II-IV disease requiring full-course chemotherapy; had measurable disease; and had cardiac, lung, renal, and liver function sufficient to tolerate chemotherapy. Patients initially received one 21-day cycle of standard rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisolone (R-CHOP; rituximab 375 mg/m2, cyclophosphamide 750 mg/m2, doxorubicin 50 mg/m2, and vincristine 1·4 mg/m2 [to a maximum of 2 mg total dose] intravenously on day 1 of the cycle, and prednisolone 100 mg orally once daily on days 1-5). During this time, we did gene-expression profiling using whole genome cDNA-mediated annealing, selection, extension, and ligation assay of tissue from routine diagnostic biopsy samples to determine the cell-of-origin subtype of each participant (germinal centre B cell, activated B cell, or unclassified). Patients were then centrally randomly assigned (1:1) via a web-based system, with block randomisation stratified by international prognostic index score and cell-of-origin subtype, to continue R-CHOP alone (R-CHOP group; control), or with bortezomib (RB-CHOP group; experimental; 1·3 mg/m2 intravenously or 1·6 mg/m2 subcutaneously) on days 1 and 8 for cycles two to six. If RNA extracted from the diagnostic tissues was of insufficient quality or quantity, participants were given R-CHOP as per the control group. The primary endpoint was 30-month progression-free survival, for the germinal centre and activated B-cell population. The primary analysis was on the modified intention-to-treat population of activated and germinal centre B-cell population. Safety was assessed in all participants who were given at least one dose of study drug. We report the progression-free survival and safety outcomes for patients in the follow-up phase after the required number of events occurred. This study was registered at ClinicalTrials.gov, number NCT01324596, and recruitment and treatment has completed for all participants, with long-term follow-up ongoing. FINDINGS: Between June 2, 2011, and June 10, 2015, 1128 eligible patients were registered, of whom 918 (81%) were randomly assigned to receive treatment (n=459 to R-CHOP, n=459 to RB-CHOP), comprising 244 (26·6%) with activated B-cell disease, 475 (51·7%) with germinal centre B cell disease, and 199 (21·7%) with unclassified disease. At a median follow-up of 29·7 months (95% CI 29·0-32·0), we saw no evidence for a difference in progression-free survival in the combined germinal centre and activated B-cell population between R-CHOP and RB-CHOP (30-month progression-free survival 70·1%, 95% CI 65·0-74·7 vs 74·3%, 69·3-78·7; hazard ratio 0·86, 95% CI 0·65-1·13; p=0·28). The most common grade 3 or worse adverse event was haematological toxicity, reported in 178 (39·8%) of 447 patients given R-CHOP and 187 (42·1%) of 444 given RB-CHOP. However, RB-CHOP was not associated with increased haematological toxicity and 398 [87·1%] of 459 participants assigned to receive RB-CHOP completed six cycles of treatment. Grade 3 or worse neuropathy occurred in 17 (3·8%) patients given RB-CHOP versus eight (1·8%) given R-CHOP. Serious adverse events occurred in 190 (42·5%) patients given R-CHOP, including five treatment-related deaths, and 223 (50·2%) given RB-CHOP, including four treatment-related deaths. INTERPRETATION: This is the first large-scale study in diffuse large B-cell lymphoma to use real-time molecular characterisation for prospective stratification, randomisation, and subsequent analysis of biologically distinct subgroups of patients. The addition of bortezomib did not improve progression-free survival. FUNDING: Janssen-Cilag, Bloodwise, and Cancer Research UK.
Asunto(s)
Protocolos de Quimioterapia Combinada Antineoplásica/administración & dosificación , Biomarcadores de Tumor/genética , Bortezomib/administración & dosificación , Perfilación de la Expresión Génica , Linfoma de Células B Grandes Difuso/tratamiento farmacológico , Inhibidores de Proteasoma/administración & dosificación , Transcriptoma , Adulto , Anciano , Anciano de 80 o más Años , Protocolos de Quimioterapia Combinada Antineoplásica/efectos adversos , Bortezomib/efectos adversos , Ciclofosfamida/administración & dosificación , Ciclofosfamida/efectos adversos , Progresión de la Enfermedad , Doxorrubicina/administración & dosificación , Doxorrubicina/efectos adversos , Femenino , Humanos , Linfoma de Células B Grandes Difuso/genética , Linfoma de Células B Grandes Difuso/inmunología , Linfoma de Células B Grandes Difuso/mortalidad , Masculino , Persona de Mediana Edad , Prednisona/administración & dosificación , Prednisona/efectos adversos , Supervivencia sin Progresión , Inhibidores de Proteasoma/efectos adversos , Rituximab/administración & dosificación , Rituximab/efectos adversos , Suiza , Factores de Tiempo , Reino Unido , Vincristina/administración & dosificación , Vincristina/efectos adversos , Adulto JovenRESUMEN
PURPOSE: Biologic heterogeneity is a feature of diffuse large B-cell lymphoma (DLBCL), and the existence of a subgroup with poor prognosis and phenotypic proximity to Burkitt lymphoma is well known. Conventional cytogenetics identifies some patients with rearrangements of MYC and BCL2 and/or BCL6 (double-hit lymphomas) who are increasingly treated with more intensive chemotherapy, but a more biologically coherent and clinically useful definition of this group is required. PATIENTS AND METHODS: We defined a molecular high-grade (MHG) group by applying a gene expression-based classifier to 928 patients with DLBCL from a clinical trial that investigated the addition of bortezomib to standard rituximab plus cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP) therapy. The prognostic significance of MHG was compared with existing biomarkers. We performed targeted sequencing of 70 genes in 400 patients and explored molecular pathology using gene expression signature databases. Findings were validated in an independent data set. RESULTS: The MHG group comprised 83 patients (9%), with 75 in the cell-of-origin germinal center B-cell-like group. MYC rearranged and double-hit groups were strongly over-represented in MHG but comprised only one half of the total. Gene expression analysis revealed a proliferative phenotype with a relationship to centroblasts. Progression-free survival rate at 36 months after R-CHOP in the MHG group was 37% (95% CI, 24% to 55%) compared with 72% (95% CI, 68% to 77%) for others, and an analysis of treatment effects suggested a possible positive effect of bortezomib. Double-hit lymphomas lacking the MHG signature showed no evidence of worse outcome than other germinal center B-cell-like cases. CONCLUSION: MHG defines a biologically coherent high-grade B-cell lymphoma group with distinct molecular features and clinical outcomes that effectively doubles the size of the poor-prognosis, double-hit group. Patients with MHG may benefit from intensified chemotherapy or novel targeted therapies.
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Protocolos de Quimioterapia Combinada Antineoplásica/uso terapéutico , Linfoma de Células B Grandes Difuso/tratamiento farmacológico , Linfoma de Células B Grandes Difuso/genética , Anticuerpos Monoclonales de Origen Murino/administración & dosificación , Protocolos de Quimioterapia Combinada Antineoplásica/administración & dosificación , Biomarcadores de Tumor/genética , Biomarcadores de Tumor/metabolismo , Bortezomib/administración & dosificación , Ciclofosfamida/administración & dosificación , Bases de Datos Genéticas , Doxorrubicina/administración & dosificación , Femenino , Humanos , Linfoma de Células B Grandes Difuso/metabolismo , Linfoma de Células B Grandes Difuso/patología , Masculino , Persona de Mediana Edad , Clasificación del Tumor , Prednisona/administración & dosificación , Modelos de Riesgos Proporcionales , Ensayos Clínicos Controlados Aleatorios como Asunto , Estudios Retrospectivos , Rituximab/administración & dosificación , Transcriptoma , Vincristina/administración & dosificaciónAsunto(s)
Linfoma de Células B Grandes Difuso/mortalidad , Adulto , Anciano , Femenino , Reordenamiento Génico/genética , Humanos , Linfoma de Células B Grandes Difuso/tratamiento farmacológico , Linfoma de Células B Grandes Difuso/genética , Masculino , Persona de Mediana Edad , Pronóstico , Proteínas Proto-Oncogénicas c-myc/genética , Reino Unido/epidemiologíaRESUMEN
BACKGROUND: Classifiers based on molecular criteria such as gene expression signatures have been developed to distinguish Burkitt lymphoma and diffuse large B cell lymphoma, which help to explore the intermediate cases where traditional diagnosis is difficult. Transfer of these research classifiers into a clinical setting is challenging because there are competing classifiers in the literature based on different methodology and gene sets with no clear best choice; classifiers based on one expression measurement platform may not transfer effectively to another; and, classifiers developed using fresh frozen samples may not work effectively with the commonly used and more convenient formalin fixed paraffin-embedded samples used in routine diagnosis. METHODS: Here we thoroughly compared two published high profile classifiers developed on data from different Affymetrix array platforms and fresh-frozen tissue, examining their transferability and concordance. Based on this analysis, a new Burkitt and diffuse large B cell lymphoma classifier (BDC) was developed and employed on Illumina DASL data from our own paraffin-embedded samples, allowing comparison with the diagnosis made in a central haematopathology laboratory and evaluation of clinical relevance. RESULTS: We show that both previous classifiers can be recapitulated using very much smaller gene sets than originally employed, and that the classification result is closely dependent on the Burkitt lymphoma criteria applied in the training set. The BDC classification on our data exhibits high agreement (~95 %) with the original diagnosis. A simple outcome comparison in the patients presenting intermediate features on conventional criteria suggests that the cases classified as Burkitt lymphoma by BDC have worse response to standard diffuse large B cell lymphoma treatment than those classified as diffuse large B cell lymphoma. CONCLUSIONS: In this study, we comprehensively investigate two previous Burkitt lymphoma molecular classifiers, and implement a new gene expression classifier, BDC, that works effectively on paraffin-embedded samples and provides useful information for treatment decisions. The classifier is available as a free software package under the GNU public licence within the R statistical software environment through the link http://www.bioinformatics.leeds.ac.uk/labpages/softwares/ or on github https://github.com/Sharlene/BDC.