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Predicting metastasized seminoma using gene expression.

Abstract：

UNLABELLED:Treatment options for testis cancer depend on the histological subtype as well as on the clinical stage. An accurate staging is essential for correct treatment. The 'golden standard' for staging purposes is CT, but occult metastasis cannot be detected with this method. Currently, parameters such as primary tumour size, vessel invasion or invasion of the rete testis are used for predicting occult metastasis. Last year the association of these parameters with metastasis could not be validated in a new independent cohort. Gene expression analysis in testis cancer allowed discrimination between the different histological subtypes (seminoma and non-seminoma) as well as testis cancer and normal testis tissue. In a two-stage study design we (i) screened the whole genome (using human whole genome microarrays) for candidate genes associated with the metastatic stage in seminoma and (ii) validated and quantified gene expression of our candidate genes (real-time quantitative polymerase chain reaction) on another independent group. Gene expression measurements of two of our candidate genes (dopamine receptor D1 [DRD1] and family with sequence similarity 71, member F2 [FAM71F2]) examined in primary testis cancers made it possible to discriminate the metastasis status in seminoma. The discriminative ability of the genes exceeded the predictive significance of currently used histological/pathological parameters. Based on gene expression analysis the present study provides suggestions for improved individual decision making either in favour of early adjuvant therapy or increased surveillance. OBJECTIVE:To evaluate the usefulness of gene expression profiling for predicting metastatic status in testicular seminoma at the time of first diagnosis compared with established clinical and pathological parameters. PATIENTS AND METHODS:Total RNA was isolated from testicular tumours of metastasized patients (12 patients, clinical stage IIa-III), non-metastasized patients (40, clinical stage I) and adjacent 'normal' tissue (n = 36). The RNA was then converted into cDNA and real-time quantitative polymerase chain reaction was run on 94 candidate genes selected from previous work. Normalised gene expression of these genes and histological variables, e.g. tumour size and rete testis infiltration, were analysed using logistic regression analysis. RESULTS:Expression of two genes (dopamine receptor D1 [DRD1] and family with sequence similarity 71, member F2 [FAM71F2], P = 0.005 and 0.024 in separate analysis and P = 0.004 and 0.016 when combining both genes, respectively) made it possible to significantly discriminate the metastasis status. Concordance increased from 77.9% (DRD1) and 72.3% (FAM71F2) in separate analysis and up to 87.7% when combining both genes in one model. Only primary tumour size in separate analysis (continuous or categorical with tumour size >6 cm) was significantly associated with metastasis (P = 0.039/P = 0.02), but concordance was lower (61%). When we combined tumour size with our two genes in one model there was no further statistical improvement or increased concordance. CONCLUSION:Based on gene expression analysis our study provides suggestions for improved individual decision making either in favour of early adjuvant therapy or increased surveillance.

journal_name

BJU Int

journal_title

BJU international

authors

Ruf CG,Linbecker M,Port M,Riecke A,Schmelz HU,Wagner W,Meineke V,Abend M

doi

10.1111/j.1464-410X.2011.10778.x

subject

Has Abstract

pub_date

2012-07-01 00:00:00

pages

E14-20

issue

2 Pt 2

eissn

1464-4096

issn

1464-410X

journal_volume

110

pub_type

杂志文章

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Predicting metastasized seminoma using gene expression.