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Discriminating lymphomas and reactive lymphadenopathy in lymph node biopsies by gene expression profiling.

ABSTRACT: BACKGROUND: Diagnostic accuracy of lymphoma, a heterogeneous cancer, is essential for patient management. Several ancillary tests including immunophenotyping, and sometimes cytogenetics and PCR are required to aid histological diagnosis. In this proof of principle study, gene expression microarray was evaluated as a single platform test in the differential diagnosis of common lymphoma subtypes and reactive lymphadenopathy (RL) in lymph node biopsies. METHODS: 116 lymph node biopsies diagnosed as RL, classical Hodgkin lymphoma (cHL), diffuse large B cell lymphoma (DLBCL) or follicular lymphoma (FL) were assayed by mRNA microarray. Three supervised classification strategies (global multi-class, local binary-class and global binary-class classifications) using diagonal linear discriminant analysis was performed on training sets of array data and the classification error rates calculated by leave one out cross-validation. The independent error rate was then evaluated by testing the identified gene classifiers on an independent (test) set of array data. RESULTS: The binary classifications provided prediction accuracies, between a subtype of interest and the remaining samples, of 88.5%, 82.8%, 82.8% and 80.0% for FL, cHL, DLBCL, and RL respectively. Identified gene classifiers include LIM domain only-2 (LMO2), Chemokine (C-C motif) ligand 22 (CCL22) and Cyclin-dependent kinase inhibitor-3 (CDK3) specifically for FL, cHL and DLBCL subtypes respectively. CONCLUSIONS: This study highlights the ability of gene expression profiling to distinguish lymphoma from reactive conditions and classify the major subtypes of lymphoma in a diagnostic setting. A cost-effective single platform "mini-chip" assay could, in principle, be developed to aid the quick diagnosis of lymph node biopsies with the potential to incorporate other pathological entities into such an assay.

SUBMITTER: Loi TH

PROVIDER: S-EPMC3080274 | biostudies-literature | 2011

REPOSITORIES: biostudies-literature

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Discriminating lymphomas and reactive lymphadenopathy in lymph node biopsies by gene expression profiling.

Loi To Ha TH Campain Anna A Bryant Adam A Molloy Tim J TJ Lutherborrow Mark M Turner Jennifer J Yang Yee Hwa Jean YH Ma David D F DD

BMC medical genomics 20110331

<h4>Background</h4>Diagnostic accuracy of lymphoma, a heterogeneous cancer, is essential for patient management. Several ancillary tests including immunophenotyping, and sometimes cytogenetics and PCR are required to aid histological diagnosis. In this proof of principle study, gene expression microarray was evaluated as a single platform test in the differential diagnosis of common lymphoma subtypes and reactive lymphadenopathy (RL) in lymph node biopsies.<h4>Methods</h4>116 lymph node biopsies ...[more]

PMID: 21453471

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Project description:BackgroundEndobronchial ultrasound (EBUS)-guided biopsy is the mainstay for investigation of mediastinal lymphadenopathy for laboratory diagnosis of malignancy, sarcoidosis, or TB. However, improved methods for discriminating between TB and sarcoidosis and excluding malignancy are still needed. We sought to evaluate the role of genomewide transcriptional profiling to aid diagnostic processes in this setting.MethodsMediastinal lymph node samples from 88 individuals were obtained by EBUS-guided aspiration for investigation of mediastinal lymphadenopathy and subjected to transcriptional profiling in addition to conventional laboratory assessments. Computational strategies were used to evaluate the potential for using the transcriptome to distinguish between diagnostic categories.ResultsMolecular signatures associated with granulomas or neoplastic and metastatic processes were clearly discernible in granulomatous and malignant lymph node samples, respectively. Support vector machine (SVM) learning using differentially expressed genes showed excellent sensitivity and specificity profiles in receiver operating characteristic curve analysis with area under curve values > 0.9 for discriminating between granulomatous and nongranulomatous disease, TB and sarcoidosis, and between cancer and reactive lymphadenopathy. A two-step decision tree using SVM to distinguish granulomatous and nongranulomatous disease, then between TB and sarcoidosis in granulomatous cases, and between cancer and reactive lymphadenopathy in nongranulomatous cases, achieved > 90% specificity for each diagnosis and afforded greater sensitivity than existing tests to detect TB and cancer. In some diagnostically ambiguous cases, computational classification predicted granulomatous disease or cancer before pathologic abnormalities were evident.ConclusionsMachine learning analysis of transcriptional profiling in mediastinal lymphadenopathy may significantly improve the clinical utility of EBUS-guided biopsies.

Project description:Sentinel lymph node, the first node draining the primary tumor, is a key component of tumor microenvironment promoting immune tolerance. In melanoma, sentinel node is one of the most important prognostic factors and the most frequent site of regional metastasis. To unravel the immunomodulatory pathways that are triggered by melanoma cells in the draining node that allow tumor spreading, transcriptional profiles associated to disease progression were analyzed in archival sentinel node biopsies (SNB). Gene expression profiles of melanoma-positive and negative SNB selected to maximize the differences in terms of disease stage and course, revealed subgroups correlating with regional node involvement and disease progression within positive biopsies. Transcriptional profiles revealed that genes showing differential expression between tumor-positive SNB with/without disease progression were mainly related to inflammatory response and that were mostly down regulated in patients with poor prognosis. TNFRSF8 encoding CD30 showing up regulation in SNB from patients with progressing disease displayed higher expression by immunohistochemical staining compared to SNB from non progressing patients. Subpopulations of CD30 positive CD4/CD8 double negative and CD4 Foxp3/PD-1 CD147 positive T cells were identified by flow cytometry analysis in metastatic nodes, suggesting a potential role of regulatory and tolerogenic T cells in melanoma progression. Cutaneous melanoma patients undergoing SNB biopsy in 2001-2004 with available 5–year follow-up clinical data were selected. Data relative to 752 cases was extracted, 80% (n= 603) with a negative SNB and 20% (n=149) with SNB positivity for melanoma metastases. The latter underwent regional lymphadenectomy by CLND and 30% (n=44) resulted positive for melanoma metastases at non-sentinel regional lymph nodes while 70% (n=105) resulted negative. Analysis of follow-up data showed that disease recurrence at 5 yrs occurred in 9% SNB negative patients, 14% of the patients with positive SNB resulting negative at CLND, and in 57% of the patients with positive SNB resulting positive at CLND, consistent to the range of previously reported rates (Santinami M 2009, Balch CM 2009). In order to exploit gene expression profiles to unravel molecular modifications occurring in SNB from patients with progressing disease we selected two groups of cases representing the extremes of the survey, i.e. patients positive at CLND (stage IIIB-C) recurring within 5 years follow-up (SNB-PP), and patients negative at CLND (stage IIIA-B) and non-relapsing at 5 years (SNB-PN). In addition, a group of negative SNB patients without disease recurrence at 5 years was selected and analysed for comparison as tumor negative SNB (SNB-N).

Dataset Information

Discriminating lymphomas and reactive lymphadenopathy in lymph node biopsies by gene expression profiling.

Publications

Discriminating lymphomas and reactive lymphadenopathy in lymph node biopsies by gene expression profiling.

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