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Clinicogenetic Risk Prognostication for First-Line Treatment of Symptomatic Follicular Lymphoma

ABSTRACT: Background: Follicular lymphoma (FL) is an indolent malignancy of germinal center B cells with highly variable patient outcomes. Recently, a 23-gene predictor score was proposed for predicting progression-free survival. In the past, we had shown that the m7-FLIPI, a clinico-genetic risk model, allows to improve patient stratification compared to clinical risk models alone. The multitude of prognostic tools in FL raises the question whether they identify common biology. Methods: In this study, we applied a modified risk score (MRS) to an independent gene-expression dataset of FL patients treated with rituximab in combination with chemotherapy. Results: Using supervised and unsupervised approaches, we showed that the MRS identifies patient groups with diverging outcomes in our dataset. In addition, using gene set enrichment and network identification, we discovered associations between the MRS, the m7-FLIPI, EZH2 mutation status and FOXP1 expression. Conclusions: Our findings lend support to expression of dark-zone related genes as a key determinant of poor outcome following rituximab and chemotherapy.

ORGANISM(S): Homo sapiens

PROVIDER: GSE119214 | GEO | 2019/02/19

REPOSITORIES: GEO

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Project description:When making treatment decisions, oncologists often stratify breast cancers into a low-risk group (ER+, low grade); an intermediate-risk group (ER+, high grade); and a high-risk group that includes Her2+ and triple-negative (ER-/PR-/Her2-) tumors. None of the currently available gene signatures correlates to this clinical classification. We aimed to develop a test that is practical for the oncologists, that offers both molecular characterization of BCs, and improved prediction of prognosis and treatment response. We investigated the molecular basis of such clinical practice by grouping Her2+ and triple-negative breast cancers together during clustering analyses on the genome-wide gene expression profiles of our training cohort, mostly derived from fine needle aspiration biopsies (FNABs) of 149 consecutive evaluable Breast cancers. The analyses consistently divided these tumors into a three-cluster pattern, similar to clinical risk-stratification groups, that was reproducible in published microarray databases (n=2487) annotated with clinical outcomes. The clinicopathologic parameters of each of these three molecular groups were also similar to clinical classification. The low-risk group had good outcomes and benefited from endocrine therapy. Both intermediate- and high-risk groups had poor outcomes and were resistant to endocrine therapy. The latter demonstrated the highest rate of complete pathological response to neoadjuvant chemotherapy; the highest activities in MYC, E2F1, Ras, β-Catenin and IFN-γ pathways; and poor prognosis predicted by 14 independent prognostic signatures. Based on a multivariate analysis, this new gene signature, termed ClinicoMolecular Triad Classification, predicted recurrence and treatment response better than all pathologic parameters and other prognostic signatures. 149 invasive breast cancers from the 172 specimens contained 161 tumors were used in this study. Expression data of the 11 tumors with replicate was separately combined before analysis.

Dataset Information

Clinicogenetic Risk Prognostication for First-Line Treatment of Symptomatic Follicular Lymphoma

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