Developing a Multidimensional Prognostic Test for Follicular Lymphoma.

Blood ◽  
2007 ◽  
Vol 110 (11) ◽  
pp. 2610-2610
Author(s):  
Cheryl J. Foster ◽  
Tara Baetz ◽  
Roland Somogyi ◽  
Larry D. Greller ◽  
Roger Sidhu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Follicular Lymphoma ◽  
Gene Pair ◽  
Conflicts Of Interest ◽  
International Prognostic Index ◽  
Patent Application ◽  
Supplementary Information ◽  
Survival Prediction ◽  
Expression Data ◽  
Queen's University

Abstract Background: Follicular lymphoma (FL) is the second most common type of non-Hodgkin lymphoma in the Western world. It is generally an indolent disease, however some patients experience rapid clinical progression. Identification of this subset of patients at the time of initial diagnosis would allow for more informed decisions to be made regarding clinical management. Methods: We investigated whether a multi-dimensional profiling approach using gene expression microarrays, a tissue microarray (TMA) and baseline clinical parameters, would allow for survival prediction for FL patients. Sixty-seven cases of FL were identified, of which high quality gene expression data were obtained with a minimum of 5 years of follow-up for 41 patients. Expression data were subjected to Predictive Interaction Analysis (PIA) to identify pairs of interacting genes that predict poor outcome, defined as death within five years of diagnosis. A TMA of all 67 patients was subjected to immunohistochemistry for markers routinely used in lymphoma diagnosis, and for numerous proteins whose relevance to oncogenesis is well-established, including p53, bcl-2, bcl-6, MUM1, p16 and p65. Results: Gene expression analysis revealed numerous genes that are highly predictive of clinical outcome. Many of these genes are known to be involved in pathways that regulate apoptosis, cell survival, proliferation and hematological function. The highly predictive single genes included BMX, NOTCH2, TFF3, BIRC4 and RIPK5, which have established roles in promoting or antagonizing apoptosis. The PIA approach further identified numerous pairs of genes that together possess greater predictive power than their individual constituent genes. Subsequent Kaplan-Meier analysis indicated that segregation of the cases according to the top performing gene pair, LOXL3 and NTS, produced two groups of cases with significantly different survival. This gene pair was able to further differentiate patient outcomes following stratification of the cases according to the Follicular Lymphoma International Prognostic Index (FLIPI), indicating its utility in providing supplementary information to the FLIPI. Upon analysis of the TMA results, detectable expression of p53 in lymphoma cells, along with clinical involvement of multiple nodal sites and B symptoms emerged as significant predictors of overall survival. Further IHC results examining expression of proteins identified as highly predictive at the transcript level will be presented. Conclusions: Our results support the utility of our profiling approach for the identification of candidate biomarkers in follicular lymphoma. Queen’s University and Biosystemix Ltd are co-owners of the intellectual property and are respectively the assignees of a provisional patent application filed at the US PTO in Sept. 2007. Roland Somogyi PhD and Larry D. Greller PhD as founding directors retain ownership positions in Biosystemix Ltd, a privately held company incorporated in Canada. Queen’s University, Biosystemix, and all the co-authors believe and agree to the best of our respective knowledge that there are no conflicts of interest in how the study was initiated, conducted, analyzed, and reported.

scholarly journals Personally Tailored Survival Prediction of Patients With Follicular Lymphoma Using Machine Learning Transcriptome-Based Models

2022 ◽  
Vol 11 ◽  
Author(s):  
Adrián Mosquera Orgueira ◽  
Miguel Cid López ◽  
Andrés Peleteiro Raíndo ◽  
Aitor Abuín Blanco ◽  
Jose Ángel Díaz Arias ◽  
...  
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Follicular Lymphoma ◽  
Survival Prediction ◽  
Good Prediction ◽  
Expression Data ◽  
Simplified Approach ◽  
High Prediction ◽  
Test Sets ◽  
Using Data

Follicular Lymphoma (FL) has a 10-year mortality rate of 20%, and this is mostly related to lymphoma progression and transformation to higher grades. In the era of personalized medicine it has become increasingly important to provide patients with an optimal prediction about their expected outcomes. The objective of this work was to apply machine learning (ML) tools on gene expression data in order to create individualized predictions about survival in patients with FL. Using data from two different studies, we were able to create a model which achieved good prediction accuracies in both cohorts (c-indexes of 0.793 and 0.662 in the training and test sets). Integration of this model with m7-FLIPI and age rendered high prediction accuracies in the test set (cox c-index 0.79), and a simplified approach identified 4 groups with remarkably different outcomes in terms of survival. Importantly, one of the groups comprised 27.35% of patients and had a median survival of 4.64 years. In summary, we have created a gene expression-based individualized predictor of overall survival in FL that can improve the predictions of the m7-FLIPI score.

Prognostic significance of bcl-xL gene expression and apoptotic cell counts in follicular lymphoma

Blood ◽  
2004 ◽  
Vol 103 (2) ◽  
pp. 695-697 ◽  
Author(s):  
Wei-Li Zhao ◽  
Marjan Ertault Daneshpouy ◽  
Nicolas Mounier ◽  
Josette Brière ◽  
Christophe Leboeuf ◽  
...  
Keyword(s):  
Gene Expression ◽  
Follicular Lymphoma ◽  
Apoptotic Cell ◽  
International Prognostic Index ◽  
Prognostic Significance ◽  
Prognostic Index ◽  
Pcr Analysis ◽  
Survival Times ◽  
Lymphoma Cells ◽  
Cell Counts

Abstract bcl-xL, a member of the Bcl-2 family, exerts an antiapoptotic effect on lymphocytes. To assess its clinical significance in patients with follicular lymphoma, realtime quantitative reverse transcription–polymerase chain reaction (RT-PCR) analysis of bcl-xL gene expression was investigated in whole lymph node sections and laser-microdissected lymphoma cells of 27 patients. Compared with 10 patients with reactive follicular hyperplasia, the bcl-xL gene was overexpressed in patients with follicular lymphoma at a higher level in microdissected lymphoma cells. The bcl-xL gene level correlated with the number of apoptotic lymphoma cells labeled by terminal deoxytransferase-catalyzed DNA nick-end labeling (TUNEL) assays (r = -0.7736). Clinically, a high bcl-xL level was significantly associated with multiple sites of extranodal involvement (P = .0020), elevated lactate dehydrogenase level (P = .0478), and an International Prognostic Index indicating high risk (P = .0235). Moreover, bcl-xL gene overexpression was linked to short overall survival times (P = .0129). The value of bcl-xL gene expression as a prognostic marker in follicular lymphoma should thus be considered.

scholarly journals Using multiple measurements of tissue to estimate subject- and cell-type-specific gene expression

2019 ◽  
Vol 36 (3) ◽  
pp. 782-788 ◽  
Author(s):  
Jiebiao Wang ◽  
Bernie Devlin ◽  
Kathryn Roeder
Keyword(s):  
Gene Expression ◽  
Empirical Bayes ◽  
Brain Regions ◽  
Tissue Level ◽  
Supplementary Information ◽  
Specific Gene ◽  
Expression Data ◽  
Cell Type ◽  
Multiple Measurements ◽  
Cell Type Specific

Abstract Motivation Patterns of gene expression, quantified at the level of tissue or cells, can inform on etiology of disease. There are now rich resources for tissue-level (bulk) gene expression data, which have been collected from thousands of subjects, and resources involving single-cell RNA-sequencing (scRNA-seq) data are expanding rapidly. The latter yields cell type information, although the data can be noisy and typically are derived from a small number of subjects. Results Complementing these approaches, we develop a method to estimate subject- and cell-type-specific (CTS) gene expression from tissue using an empirical Bayes method that borrows information across multiple measurements of the same tissue per subject (e.g. multiple regions of the brain). Analyzing expression data from multiple brain regions from the Genotype-Tissue Expression project (GTEx) reveals CTS expression, which then permits downstream analyses, such as identification of CTS expression Quantitative Trait Loci (eQTL). Availability and implementation We implement this method as an R package MIND, hosted on https://github.com/randel/MIND. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals iMIRAGE: an R package to impute microRNA expression using protein-coding genes

2019 ◽  
Vol 36 (8) ◽  
pp. 2608-2610
Author(s):  
Aritro Nath ◽  
Jeremy Chang ◽  
R Stephanie Huang
Keyword(s):  
Gene Expression ◽  
Small Rnas ◽  
Transcriptional Regulators ◽  
R Package ◽  
Machine Learning Algorithms ◽  
Supplementary Information ◽  
Expression Data ◽  
Protein Coding ◽  
Altered Protein ◽  
Independent Test

Abstract Summary MicroRNAs (miRNAs) are critical post-transcriptional regulators of gene expression. Due to challenges in accurate profiling of small RNAs, a vast majority of public transcriptome datasets lack reliable miRNA profiles. However, the biological consequence of miRNA activity in the form of altered protein-coding gene (PCG) expression can be captured using machine-learning algorithms. Here, we present iMIRAGE (imputed miRNA activity from gene expression), a convenient tool to predict miRNA expression using PCG expression of the test datasets. The iMIRAGE package provides an integrated workflow for normalization and transformation of miRNA and PCG expression data, along with the option to utilize predicted miRNA targets to impute miRNA activity from independent test PCG datasets. Availability and implementation The iMIRAGE package for R, along with package documentation and vignette, is available at https://aritronath.github.io/iMIRAGE/index.html. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Enhancing the Lasso Approach for Developing a Survival Prediction Model Based on Gene Expression Data

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Shuhei Kaneko ◽  
Akihiro Hirakawa ◽  
Chikuma Hamada
Keyword(s):  
Gene Expression ◽  
Prediction Model ◽  
Gene Expression Data ◽  
Prediction Models ◽  
Mixture Distribution ◽  
Tuning Parameter ◽  
Survival Prediction ◽  
Expression Data ◽  
True Positive ◽  
False Negatives

In the past decade, researchers in oncology have sought to develop survival prediction models using gene expression data. The least absolute shrinkage and selection operator (lasso) has been widely used to select genes that truly correlated with a patient’s survival. The lasso selects genes for prediction by shrinking a large number of coefficients of the candidate genes towards zero based on a tuning parameter that is often determined by a cross-validation (CV). However, this method can pass over (or fail to identify) true positive genes (i.e., it identifies false negatives) in certain instances, because the lasso tends to favor the development of a simple prediction model. Here, we attempt to monitor the identification of false negatives by developing a method for estimating the number of true positive (TP) genes for a series of values of a tuning parameter that assumes a mixture distribution for the lasso estimates. Using our developed method, we performed a simulation study to examine its precision in estimating the number of TP genes. Additionally, we applied our method to a real gene expression dataset and found that it was able to identify genes correlated with survival that a CV method was unable to detect.

scholarly journals Bayesian ensemble methods for survival prediction in gene expression data

2010 ◽  
Vol 27 (3) ◽  
pp. 359-367 ◽  
Author(s):  
Vinicius Bonato ◽  
Veerabhadran Baladandayuthapani ◽  
Bradley M. Broom ◽  
Erik P. Sulman ◽  
Kenneth D. Aldape ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Ensemble Methods ◽  
Survival Prediction ◽  
Expression Data

Lymphomas

2017 ◽  
Author(s):  
Kieron Dunleavy ◽  
Wyndham H Wilson
Keyword(s):  
Gene Expression ◽  
Follicular Lymphoma ◽  
B Cell ◽  
Hodgkin Lymphoma ◽  
Group Performance ◽  
Cell Lymphoma ◽  
International Prognostic Index ◽  
Prognostic Index ◽  
B Cell Lymphoma ◽  
Non Hodgkin Lymphoma

Lymphoma is the fifth most common type of cancer in the United States, with 74,490 new cases estimated in 2009. Approximately 15% of patients with lymphoma have Hodgkin lymphoma; the remainder have one of the non-Hodgkin lymphomas. The incidence of non-Hodgkin lymphoma has increased steadily over recent decades. This chapter reviews the epidemiology, classification, clinical features, pathology, diagnostic evaluation, staging and prognosis, and treatment of Hodgkin and non-Hodgkin lymphoma. Other topics discussed include the acute and chronic effects of therapy for Hodgkin disease, as well as the subtypes of non-Hodgkin lymphomas, including indolent B cell lymphoma, follicular lymphoma, small lymphocytic lymphoma, mantle cell lymphoma, marginal-zone lymphoma, diffuse large B cell lymphoma (DLBCL), primary central nervous system lymphoma (PCNSL), Burkitt lymphoma, and HIV-related non-Hodgkin lymphoma. Figures illustrate the cellular appearance of Hodgkin lymphoma subtypes and DLBCL, diagnosis of DLBCL subtypes by gene expression, computed tomography and plain chest film in primary mediastinal cell lymphoma, MRI of the brain in PCNSL, and gene expression and gene expression predictors of survival among patients with DLBCL treated with rituximab, cyclophosphamide, hydroxydaunorubicin, vincristine [Oncovin], and prednisone (R-CHOP). Tables describe the Ann Arbor classification and the Cotswold modification for staging of lymphoma; the International Prognostic Score for advanced Hodgkin lymphoma; the World Health Organization classification of hematopoietic neoplasms; chromosomal translocations in non-Hodgkin lymphoma; the Eastern Cooperative Oncology Group performance scale; the International Prognostic Index for aggressive non-Hodgkin lymphoma; and the Follicular Lymphoma International Prognostic Index. This chapter has 185 references. This review contains 9 tables, 7 figures and 185 references

Increasing Grades of Follicular Lymphoma Correlate with Better Prognosis in Patients Treated with Rituximab

Blood ◽  
2011 ◽  
Vol 118 (21) ◽  
pp. 1641-1641
Author(s):  
Björn Engelbrekt Wahlin ◽  
Christer Sundström ◽  
Harald Holte ◽  
Birgitta Sander ◽  
Bjørn Østenstad ◽  
...  
Keyword(s):  
Lymph Nodes ◽  
Follicular Lymphoma ◽  
Conflicts Of Interest ◽  
International Prognostic Index ◽  
Prognostic Index ◽  
World Health ◽  
Time To Treatment Failure ◽  
Immunologic Factors ◽  
Health Organization ◽  
Anti Cd20

Abstract Abstract 1641 Background: The World Health Organization (WHO) classification allocates indolent follicular lymphoma to grades 1, 2 or 3A according to the proportions of small centrocytes and large centroblasts in the neoplastic follicles. The prognostic value of the WHO's grading system in indolent follicular lymphoma has not been investigated in patients given the anti-CD20 monoclonal antibody rituximab without chemotherapy. Methods: We prospectively reviewed the follicular lymphoma grades in 276 grade 1–3A patients who received upfront rituximab without chemotherapy in two randomized trials. Flow cytometry analyses of malignant and nonmalignant lymphocyte subsets in lymph nodes and blood were also assessed. Results: In these patients given upfront rituximab-containing therapy, increasing grades of 1, 2, and 3A correlated with longer time to treatment-failure (P =0.002) and overall survival (P =0.045), independently of clinical factors (including the follicular lymphoma international prognostic index). The grades' impact was also independent of the levels of CD3+, CD4+, and CD8+ T cells in lymph nodes and in blood. Conclusion: Increasing grades of indolent follicular lymphoma correlate with better outcome in patients treated upfront with rituximab without chemotherapy, independently of clinical and immunologic factors. This suggests that treatment with rituximab acts differentially in tumors depending on the centrocyte/centroblast ratio. Disclosures: No relevant conflicts of interest to declare.

scholarly journals Model-based clustering of multi-tissue gene expression data

2019 ◽  
Author(s):  
Pau Erola ◽  
Johan L M Björkegren ◽  
Tom Michoel
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Clustering Algorithm ◽  
Expression Profiles ◽  
Individual Characteristics ◽  
Supplementary Information ◽  
Expression Data ◽  
Model Based ◽  
Tissue Gene Expression ◽  
Lemon Tree

Abstract Motivation Recently, it has become feasible to generate large-scale, multi-tissue gene expression data, where expression profiles are obtained from multiple tissues or organs sampled from dozens to hundreds of individuals. When traditional clustering methods are applied to this type of data, important information is lost, because they either require all tissues to be analyzed independently, ignoring dependencies and similarities between tissues, or to merge tissues in a single, monolithic dataset, ignoring individual characteristics of tissues. Results We developed a Bayesian model-based multi-tissue clustering algorithm, revamp, which can incorporate prior information on physiological tissue similarity, and which results in a set of clusters, each consisting of a core set of genes conserved across tissues as well as differential sets of genes specific to one or more subsets of tissues. Using data from seven vascular and metabolic tissues from over 100 individuals in the STockholm Atherosclerosis Gene Expression (STAGE) study, we demonstrate that multi-tissue clusters inferred by revamp are more enriched for tissue-dependent protein-protein interactions compared to alternative approaches. We further demonstrate that revamp results in easily interpretable multi-tissue gene expression associations to key coronary artery disease processes and clinical phenotypes in the STAGE individuals. Availability and implementation Revamp is implemented in the Lemon-Tree software, available at https://github.com/eb00/lemon-tree Supplementary information Supplementary data are available at Bioinformatics online.

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