9,717 research outputs found

    A Phase I/II first-line study of R-CHOP plus B-cell receptor/NF-κB-double-targeting to molecularly assess therapy response

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    The ImbruVeRCHOP trial is an investigator-initiated, multicenter, single-arm, open label Phase I/II study for patients 61-80 years of age with newly diagnosed CD20+ diffuse large B-cell lymphoma and a higher risk profile (International Prognostic Index ≥2). Patients receive standard chemotherapy (CHOP) plus immunotherapy (Rituximab), a biological agent (the proteasome inhibitor Bortezomib) and a signaling inhibitor (the Bruton's Tyrosine Kinase-targeting therapeutic Ibrutinib). Using an all-comers approach, but subjecting patients to another lymphoma biopsy acutely under first-cycle immune-chemo drug exposure, ImbruVeRCHOP seeks to identify an unbiased molecular responder signature that marks diffuse large B-cell lymphoma patients at risk and likely to benefit from this regimen as a double, proximal and distal B-cell receptor/NF-κB-co-targeting extension of the current R-CHOP standard of care. EudraCT-Number: 2015-003429-32; ClinicalTrials.gov identifier: NCT03129828

    Genomic and proteomic profiling for cancer diagnosis in dogs

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    Global gene expression, whereby tumours are classified according to similar gene expression patterns or ‘signatures’ regardless of cell morphology or tissue characteristics, is being increasingly used in both the human and veterinary fields to assist in cancer diagnosis and prognosis. Many studies on canine tumours have focussed on RNA expression using techniques such as microarrays or next generation sequencing. However, proteomic studies combining two-dimensional polyacrylamide gel electrophoresis or two-dimensional differential gel electrophoresis with mass spectrometry have also provided a wealth of data on gene expression in tumour tissues. In addition, proteomics has been instrumental in the search for tumour biomarkers in blood and other body fluids

    Improved personalized survival prediction of patients with diffuse large B-cell Lymphoma using gene expression profiling

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    BACKGROUND: Thirty to forty percent of patients with Diffuse Large B-cell Lymphoma (DLBCL) have an adverse clinical evolution. The increased understanding of DLBCL biology has shed light on the clinical evolution of this pathology, leading to the discovery of prognostic factors based on gene expression data, genomic rearrangements and mutational subgroups. Nevertheless, additional efforts are needed in order to enable survival predictions at the patient level. In this study we investigated new machine learning-based models of survival using transcriptomic and clinical data. METHODS: Gene expression profiling (GEP) of in 2 different publicly available retrospective DLBCL cohorts were analyzed. Cox regression and unsupervised clustering were performed in order to identify probes associated with overall survival on the largest cohort. Random forests were created to model survival using combinations of GEP data, COO classification and clinical information. Cross-validation was used to compare model results in the training set, and Harrel's concordance index (c-index) was used to assess model's predictability. Results were validated in an independent test set. RESULTS: Two hundred thirty-three and sixty-four patients were included in the training and test set, respectively. Initially we derived and validated a 4-gene expression clusterization that was independently associated with lower survival in 20% of patients. This pattern included the following genes: TNFRSF9, BIRC3, BCL2L1 and G3BP2. Thereafter, we applied machine-learning models to predict survival. A set of 102 genes was highly predictive of disease outcome, outperforming available clinical information and COO classification. The final best model integrated clinical information, COO classification, 4-gene-based clusterization and the expression levels of 50 individual genes (training set c-index, 0.8404, test set c-index, 0.7942). CONCLUSION: Our results indicate that DLBCL survival models based on the application of machine learning algorithms to gene expression and clinical data can largely outperform other important prognostic variables such as disease stage and COO. Head-to-head comparisons with other risk stratification models are needed to compare its usefulness

    Artificial Neural Network Analysis of Gene Expression Data Predicted Non-Hodgkin Lymphoma Subtypes with High Accuracy

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    Predictive analytics using artificial intelligence is a useful tool in cancer research. A multilayer perceptron neural network used gene expression data to predict the lymphoma subtypes of 290 cases of non-Hodgkin lymphoma (GSE132929). The input layer included both the whole array of 20,863 genes and a cancer transcriptome panel of 1769 genes. The output layer was lymphoma subtypes, including follicular lymphoma, mantle cell lymphoma, diffuse large B-cell lymphoma, Burkitt lymphoma, and marginal zone lymphoma. The neural networks successfully classified the cases consistent with the lymphoma subtypes, with an area under the curve (AUC) that ranged from 0.87 to 0.99. The most relevant predictive genes were LCE2B, KNG1, IGHV7_81, TG, C6, FGB, ZNF750, CTSV, INGX, and COL4A6 for the whole set; and ARG1, MAGEA3, AKT2, IL1B, S100A7A, CLEC5A, WIF1, TREM1, DEFB1, and GAGE1 for the cancer panel. The characteristic predictive genes for each lymphoma subtypes were also identified with high accuracy (AUC = 0.95, incorrect predictions = 6.2%). Finally, the topmost relevant 30 genes of the whole set, which belonged to apoptosis, cell proliferation, metabolism, and antigen presentation pathways, not only predicted the lymphoma subtypes but also the overall survival of diffuse large B-cell lymphoma (series GSE10846, n = 414 cases), and most relevant cancer subtypes of The Cancer Genome Atlas (TCGA) consortium including carcinomas of breast, colorectal, lung, prostate, and gastric, melanoma, etc. (7441 cases). In conclusion, neural networks predicted the non-Hodgkin lymphoma subtypes with high accuracy, and the highlighted genes also predicted the survival of a pan-cancer series

    Germinal Center B Cell-Like (GCB) and Activated B Cell-Like (ABC) Type of Diffuse Large B Cell Lymphoma (DLBCL): Analysis of Molecular Predictors, Signatures, Cell Cycle State and Patient Survival

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    Aiming to find key genes and events, we analyze a large data set on diffuse large B-cell lymphoma (DLBCL) gene-expression (248 patients, 12196 spots). Applying the loess normalization method on these raw data yields improved survival predictions, in particular for the clinical important group of patients with medium survival time. Furthermore, we identify a simplified prognosis predictor, which stratifies different risk groups similarly well as complex signatures

    Artificial Intelligence Predicted Overall Survival and Classified Mature B-Cell Neoplasms Based on Immuno-Oncology and Immune Checkpoint Panels

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    Artificial intelligence (AI) can identify actionable oncology biomarkers. This research integrates our previous analyses of non-Hodgkin lymphoma. We used gene expression and immunohistochemical data, focusing on the immune checkpoint, and added a new analysis of macrophages, including 3D rendering. The AI comprised machine learning (C5, Bayesian network, C&R, CHAID, discriminant analysis, KNN, logistic regression, LSVM, Quest, random forest, random trees, SVM, tree-AS, and XGBoost linear and tree) and artificial neural networks (multilayer perceptron and radial basis function). The series included chronic lymphocytic leukemia, mantle cell lymphoma, follicular lymphoma, Burkitt, diffuse large B-cell lymphoma, marginal zone lymphoma, and multiple myeloma, as well as acute myeloid leukemia and pan-cancer series. AI classified lymphoma subtypes and predicted overall survival accurately. Oncogenes and tumor suppressor genes were highlighted (MYC, BCL2, and TP53), along with immune microenvironment markers of tumor-associated macrophages (M2-like TAMs), T-cells and regulatory T lymphocytes (Tregs) (CD68, CD163, MARCO, CSF1R, CSF1, PD-L1/CD274, SIRPA, CD85A/LILRB3, CD47, IL10, TNFRSF14/HVEM, TNFAIP8, IKAROS, STAT3, NFKB, MAPK, PD-1/PDCD1, BTLA, and FOXP3), apoptosis (BCL2, CASP3, CASP8, PARP, and pathway-related MDM2, E2F1, CDK6, MYB, and LMO2), and metabolism (ENO3, GGA3). In conclusion, AI with immuno-oncology markers is a powerful predictive tool. Additionally, a review of recent literature was made

    Biological predictors of survival in limphoma and mechanisms underlying follicular lymphoma transformaion into diffuse large B cell lymphoma (vol I e II)

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    RESUMO: Os biomarcadores tumorais permitem identificar os doentes com maior risco de recorrência da doença, predizer a resposta tumoral à terapêutica e, finalmente, definir candidatos a novos alvos terapêuticos. Novos biomarcadores são especialmente necessários na abordagem clínica dos linfomas. Actualmente, esses tumores são diagnosticados através de uma combinação de características morfológicas, fenotípicas e moleculares, mas o prognóstico e o planeamento terapêutico estão quase exclusivamente dependentes de características clínicas. Estes factores clínicos são, na maioria dos linfomas, insuficientes numa proporção significativa dos doentes, em particular, aqueles com pior prognóstico. O linfoma folicular (LF) é, globalmente, o segundo subtipo mais comum de linfoma. É tipicamente uma doença indolente com uma sobrevida média entre os 8 e 12 anos, mas é geralmente fatal quando se transforma num linfoma agressivo de alto grau, habitualmente o linfoma difuso de grandes células B (LDGCB). Morfologicamente e funcionalmente, as células do LF recapitulam as células normais do centro germinativo na sua dependência de sobrevivência do microambiente não-tumoral, especialmente das células do sistema imunológico. Biomarcadores preditivos de transformação não existem pelo que um melhor conhecimento da biologia intrínseca de progressão do LF poderá revelar novos candidatos. Nesta tese descrevo duas abordagens distintas para a descoberta de novos biomarcadores. A primeira, o estudo da expressão global de genes ('genomics') obtidos por técnicas de alto rendimento que analisam todo o genoma humano sequenciado, permitindo identificar novas anomalias genéticas que possam representar mecanismos biológicos importantes de transformação. São descritos novos genes e alterações genómicas associados à transformação do LF, sendo especialmente relevantes as relacionadas com os eventos iniciais de transformação em LDGCB. A segunda, baseou-se em várias hipóteses centradas no microambiente do LF, rico em vários tipos de células nãomalignas. Os estudos imunoarquitectural de macrófagos, células T regulatórias e densidade de microvasos efectuado em biopsias de diagnóstico de doentes com LF tratados uniformemente correlacionaram-se significativamente, e independentemente dos critérios clínicos, com a evolução clínica e, mais importante, com o risco de transformação em LDGCB. Nesta tese, foram preferencialmente utilizadas (e optimizadas) técnicas que permitam o uso de amostras fixadas em parafina e formalina (FFPET). Estas são facilmente acessíveis a partir das biopsias de diagnóstico de rotina presentes nos arquivos de todos os departamentos de patologia, facilitando uma transição rápida dos novos marcadores para a prática clínica. Embora o FL fosse o tema principal da tese, os novos achados permitiram estender facilmente hipóteses semelhantes a outros subtipos de linfoma. Assim, são propostos e validados vários biomarcadores promissores e relacionados com o microambiente não tumoral, sobretudo dependentes das células do sistema imunológico, como contribuintes importantes para a biologia dos linfomas. Estes sugerem novas opções para a abordagem clínica destas doenças e, eventualmente, novos alvos terapêuticos.------------- ABSTRACT: Cancer biomarkers provide an opportunity to identify those patients most at risk for disease recurrence, predict which tumours will respond to different therapeutic approaches and ultimately define candidate biomarkers that may serve as targets for personalized therapy. New biomarkers are especially needed in the management of lymphoid cancers. At present, these tumours are diagnosed using a combination of morphologic, phenotypic and molecular features but prognosis and overall survival are mostly dependent on clinical characteristics. In most lymphoma types, these imprecisely assess a significant proportion of patients, in particular, those with very poor outcomes. Follicular lymphoma (FL) is the second most common lymphoma subtype worldwide. It is typically an indolent disease with current median survivals in the range of 8-12 years, but is usually fatal when it transforms into an aggressive high-grade lymphoma, characteristically Diffuse Large B Cell Lymphoma (DLBCL). Morphologically and functionally it recapitulates the normal cells of the germinal center with its survival dependency on non-malignant immune and immunerelated cells. Informative markers of transformation related to the intrinsic biology of FL progression are needed. Within this thesis two separate approaches to biomarker discovery were employed. The first was to study the global expression of genes (‘genomics’) obtained using high-throughput, wholegenome-wide approaches that offered the possibility for discovery of new genetic abnormalities that might represent the important biological mechanisms of transformation. Gene signatures associated with early events of transformation were found. Another approach relied on hypothesis-driven concepts focusing upon the microenvironment, rich in several non-malignant cell types. The immunoarchitectural studies of macrophages, regulatory T cells and microvessel density on diagnostic biopsies of uniformly treated FL patients significantly predicted clinical outcome and, importantly, also informed on the risk of transformation. Techniques that enabled the use of routine formalin fixed paraffin embedded diagnostic specimens from the pathology department archives were preferentially used in this thesis with the goal of fulfilling a rapid bench-to-beside” translation for these new findings. Although FL was the main subject of the thesis the new findings and hypotheses allowed easy transition into other lymphoma types. Several promising biomarkers were proposed and validated including the implication of several non-neoplastic immune cells as important contributors to lymphoma biology, opening new options for better treatment planning and eventually new therapeutic targets and candidate therapeutics

    Longitudinal expression profiling identifies a poor risk subset of patients with ABC-type Diffuse Large B Cell Lymphoma.

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    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 falling short of providing a consistent relapse-specific genetic signature. In our study, we have focussed attention on the changes in gene expression profile accompanying DLBCL relapse using archival paired diagnostic/relapse specimens from 38 de novo DLBCL patients. Cell of origin remained stable from diagnosis to relapse in 84% of patients, with only a single patient showing COO switching from ABC to 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
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