Prediction of Survival in Follicular Lymphoma Based on Molecular Features of Tumor-Infiltrating Immune Cells

University of Rochester, Rochester, New York, United States
New England Journal of Medicine (Impact Factor: 55.87). 12/2004; 351(21):2159-69. DOI: 10.1056/NEJMoa041869
Source: PubMed


Patients with follicular lymphoma may survive for periods of less than 1 year to more than 20 years after diagnosis. We used gene-expression profiles of tumor-biopsy specimens obtained at diagnosis to develop a molecular predictor of the length of survival.
Gene-expression profiling was performed on 191 biopsy specimens obtained from patients with untreated follicular lymphoma. Supervised methods were used to discover expression patterns associated with the length of survival in a training set of 95 specimens. A molecular predictor of survival was constructed from these genes and validated in an independent test set of 96 specimens.
Individual genes that predicted the length of survival were grouped into gene-expression signatures on the basis of their expression in the training set, and two such signatures were used to construct a survival predictor. The two signatures allowed patients with specimens in the test set to be divided into four quartiles with widely disparate median lengths of survival (13.6, 11.1, 10.8, and 3.9 years), independently of clinical prognostic variables. Flow cytometry showed that these signatures reflected gene expression by nonmalignant tumor-infiltrating immune cells.
The length of survival among patients with follicular lymphoma correlates with the molecular features of nonmalignant immune cells present in the tumor at diagnosis.

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Available from: Elaine S Jaffe, Dec 10, 2015
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    • "Most demonstrate an association of T cells and Tregs with favourable outcome ( Carreras et al , 2006 ; Lee et al , 2006 ; Wahlin et al , 2007 ; Carreras et al , 2009 ; de Jong et al , 2009 ; Farinha et al , 2010 ; Gribben , 2010 ; Solal - Céligny et al , 2010 ; Wahlin et al , 2010 ; de Jong and Fest , 2011 ; Wahlin et al , 2011 ; Koch et al , 2012 ) although some report an unfavourable outcome ( Richendollar et al , 2011 ) . Discrepancy between studies may be owing to differences in precision of T - cell subset identification , or in pattern analysis , the pattern of T cells being important for prognosis ( Lee et al , 2006 ; de Jong et al , 2009 ; Farinha et al , 2010 ) , whilst the present study concords with the majority of previous studies in which higher levels of T cells are associated with longer survival in FL ( Dave et al , 2004 ) . Although there is a large body of evidence supporting a favourable prognostic role for T cells in general , Tregs , which are a subset of CD4 þ cells , are of particular importance . "
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    ABSTRACT: Background: Histopathological prognostication relies on morphological pattern recognition, but as numbers of biomarkers increase, human prognostic pattern-recognition ability decreases. Follicular lymphoma (FL) has a variable outcome, partly determined by FOXP3 Tregs. We have developed an automated method, hypothesised interaction distribution (HID) analysis, to analyse spatial patterns of multiple biomarkers which we have applied to tumour-infiltrating lymphocytes in FL. Methods: A tissue microarray of 40 patient samples was used in triplex immunohistochemistry for FOXP3, CD3 and CD69, and multispectral imaging used to determine the numbers and locations of CD3(+), FOXP3/CD3(+) and CD69/CD3(+) T cells. HID analysis was used to identify associations between cellular pattern and outcome. Results: Higher numbers of CD3(+) (P=0.0001), FOXP3/CD3(+) (P=0.0031) and CD69/CD3(+) (P=0.0006) cells were favourable. Cross-validated HID analysis of cell pattern identified patient subgroups with statistically significantly different survival (35.5 vs 142 months, P=0.00255), a more diffuse pattern associated with favourable outcome and an aggregated pattern with unfavourable outcome. Conclusions: A diffuse pattern of FOXP3 and CD69 positivity was favourable, demonstrating ability of HID analysis to automatically identify prognostic cellular patterns. It is applicable to large numbers of biomarkers, representing an unsupervised, automated method for identification of undiscovered prognostic cellular patterns in cancer tissue samples.British Journal of Cancer advance online publication 6 October 2015. doi:10.1038/bjc.2015.291
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    • "The similarity between the gene expression profile (GEP) of CLL cells isolated from lymph nodes (Herishanu et al., 2011) and GEP of CLL cells after coculture with NLCs (Burger et al., 2009) suggest that NLCs are a valid model system for the CLL lymph node microenvironment . Comparable to NLCs in CLL are monocyte-derived lymphoma associated macrophages (LAM), which have prognostic significance in follicular lymphoma (Dave et al., 2004) and diffuse large B cell lymphoma (Lenz et al., 2008), suggesting that similar supportive molecular interactions exist between LAM and lymphoma cells. CXCR4 (Burger et al., 1999) and CXCR5 (Burkle et al., 2007) chemokine receptors are expressed at high levels on CLL cells, which allows CLL cells to sense and follow CXCL12 and CXCL13 chemokine gradients, established by tissue stromal cells. "
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    • "Ranking methods include the t-statistic for difference in expression of good versus bad prognosis genes (Bell et al., 2011; Verhaak et al., 2013) and signed averaging of discretized or continuous expression values (Colman et al., 2010; Dave et al., 2004; Hallett et al., 2010; Kang et al., 2012;Rè me et al., 2013). Replacing lasso coefficients by their signs has been proposed for summarizing gene pathway activity (Eng et al., 2013). "
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    ABSTRACT: Motivation: The successful translation of genomic signatures into clinical settings relies on good discrimination between patient subgroups. Many sophisticated algorithms have been proposed in the statistics and machine learning literature, but in practice simpler algorithms are often used. However, few simple algorithms have been formally described or systematically investigated. Results: We give a precise definition of a popular simple method we refer to as más-o-menos, which calculates prognostic scores for discrimination by summing standardized predictors, weighted by the signs of their marginal associations with the outcome. We study its behavior theoretically, in simulations and in an extensive analysis of 27 independent gene expression studies of bladder, breast and ovarian cancer, altogether totaling 3833 patients with survival outcomes. We find that despite its simplicity, más-o-menos can achieve good discrimination performance. It performs no worse, and sometimes better, than popular and much more CPU-intensive methods for discrimination, including lasso and ridge regression. Availability and implementation: Más-o-menos is implemented for survival analysis as an option in the survHD package, available from and submitted to Bioconductor.
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