A pluripotency signature predicts histologic transformation and influences survival in follicular lymphoma patients

Abstract

Histologic transformation (HT) of follicular lymphoma to diffuse large B-cell lymphoma (DLBCL-t) is associated with accelerated disease course and drastically worse outcome, yet the underlying mechanisms are poorly understood. We show that a network of gene transcriptional modules underlies HT. Central to the network hierarchy is a signature strikingly enriched for pluripotency-related genes. These genes are typically expressed in embryonic stem cells (ESCs), including MYC and its direct targets. This core ESC-like program was independent of proliferation/cell-cycle and overlapped but was distinct from normal B-cell transcriptional programs. Furthermore, we show that the ESC program is correlated with transcriptional programs maintaining tumor phenotype in transgenic MYC-driven mouse models of lymphoma. Although our approach was to identify HT mechanisms rather than to derive an optimal survival predictor, a model based on ESC/ differentiation programs stratified patient outcomes in 2 independent patient co-

Transformed follicular non-Hodgkin lymphoma

“…on May 12, 2018. by guest www.bloodjournal.org From gene expression pattern that Gentles et al correlated with a propensity to transform. 22 Similarly, the observation that a small gene expression panel based on components of the nuclear factor kB (NF-kB) pathway components is predictive of HT may directly reflect the recent sequencing data showing that HT is frequently accompanied by mutations in genes encoding components of the NF-kB pathway, as mutations affecting the NF-kB pathway appear to be among the most consistent of the stepwise changes that ultimately lead to clinically recognizable HT. 3,23 Summary: biology of transformation…”

Expression profiles of adult T-cell leukemia–lymphoma and associations with clinical responses to zidovudine and interferon α

“…For display purposes, paired samples were displayed as the difference in log-ratios between the ‘B’ and ‘A’ samples, and then hierarchically clustered. To assess biological enrichment in response to AZT–IFN α , we applied Gene Set Enrichment Analysis (GSEA v2.0.4, using default parameters) [22] to the pre-ranked list of features as defined above using the paired SAM statistic; we supplemented gene sets within the Molecular Signatures Database (mSIGDB; Broad Institute) [22] with those from the immunological Signatures Database (SIGDB; National Cancer Institute, NIH) [23] as previously described [24]. For supervised analyses of gene expression differences among responding and non-responding patients to AZT–IFN α , SAM was applied to all pre-therapy (‘A’) patient specimens, starting with the data matrix of 9606 features described above.…”

Indolent lymphoma: follicular lymphoma and the microenvironment—insights from gene expression profiling

“…That study further described a decrease in a "stromal signature," particularly genes that were targets of TGF-␤, which together with ESC signatures predicted both transformation and survival. 36 Other investigators evaluated mechanisms of FL transformation using a short oligonucleotide technique. Similar to the previously cited studies, these investigators also reported transformation as a heterogenous process with stepwise alternative pathways.…”