Article

Prediction of survival in follicular lymphoma based on molecular features of tumor-infiltrating immune cells.

National Cancer Institute, NIH, Bethesda, Md 20892, USA.
New England Journal of Medicine (Impact Factor: 54.42). 12/2004; 351(21):2159-69. DOI: 10.1056/NEJMoa041869
Source: PubMed

ABSTRACT 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.

0 Bookmarks
 · 
220 Views
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Neoadjuvant chemotherapy for breast cancer leads to considerable variability in clinical responses, with only 10 to 20% of cases achieving complete pathologic responses (pCR). Biological and clinical factors that determine the extent of pCR are incompletely understood. Mounting evidence indicates that the patient's immune system contributes to tumor regression and can be modulated by therapies. The cell types most frequently observed with this association are effector tumor infiltrating lymphocytes (TILs), such as cytotoxic T cells, natural killer cells and B cells. We and others have shown that the relative abundance of TILs in breast cancer can be quantified by intratumoral transcript levels of coordinately expressed, immune cell-specific genes. Through expression microarray analysis, we recently discovered three immune gene signatures, or metagenes, that appear to reflect the relative abundance of distinct tumor-infiltrating leukocyte populations. The B/P (B cell/plasma cell), T/NK (T cell/natural killer cell) and M/D (monocyte/dendritic cell) immune metagenes were significantly associated with distant metastasis-free survival of patients with highly proliferative cancer of the basal-like, HER2-enriched and luminal B intrinsic subtypes. Given the histopathological evidence that TIL abundance is predictive of neoadjuvant treatment efficacy, we evaluated the therapy-predictive potential of the prognostic immune metagenes. We hypothesized that pre-chemotherapy immune gene signatures would be significantly predictive of tumor response. In a multi-institutional, meta-cohort analysis of 701 breast cancer patients receiving neoadjuvant chemotherapy, gene expression profiles of tumor biopsies were investigated by logistic regression to determine the existence of therapy-predictive interactions between the immune metagenes, tumor proliferative capacity, and intrinsic subtypes. By univariate analysis, the B/P, T/NK and M/D metagenes were all significantly and positively associated with favorable pathologic responses. In multivariate analyses, proliferative capacity and intrinsic subtype altered the significance of the immune metagenes in different ways, with the M/D and B/P metagenes achieving the greatest overall significance after adjustment for other variables. Gene expression signatures of infiltrating immune cells carry both prognostic and therapy-predictive value that is impacted by tumor proliferative capacity and intrinsic subtype. Anti-tumor functions of plasma B cells and myeloid-derived antigen-presenting cells may explain more variability in pathologic response to neoadjuvant chemotherapy than previously recognized.
    Genome Medicine 10/2014; 6(10):80. · 4.94 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: We aim at obtaining a prognostic on the survival time adjusted on covariates in a high-dimensional setting. Towards this end, we consider a conditional hazard rate function that does not rely on an underlying model and we estimate it by the best Cox's proportional hazards model given two dictionaries of functions. The first dictionary is used to construct an approximation of the logarithm of the baseline hazard function and the second to approximate the relative risk. Since we are in high-dimension, we consider the Lasso procedure to estimate the unknown parameters of the best Cox's model approximating the conditional hazard rate function. We provide non-asymptotic oracle inequalities for the Lasso estimator of the conditional hazard risk function. Our results are mainly based on an empirical Bernstein's inequalities for martingales with jumps.
  • [Show abstract] [Hide abstract]
    ABSTRACT: The prognosis of follicular lymphoma (FL) has significantly improved over the last decade, particularly following the introduction of the anti-CD20 monoclonal antibody rituximab, which has challenged the old concept of FL as an incurable disease. However, the decision whether to start treatment in a patient with advanced FL or adopt a watch-and-wait policy remains a subject of controversy. Furthermore, the optimal first-line treatment for FL remains a clinical challenge owing to the numerous different therapeutic options available. In this review, the authors focus on the initial management of patients with newly diagnosed FL, consider the different treatment options for every stage, paying special consideration to the therapeutic approaches for each clinical scenario, and discuss future directions.
    Future Oncology 11/2014; 10(12):1967-1980. · 2.61 Impact Factor