Purpose

Preclinical and early clinical data suggested that combining histone deacetylase (HDAC) and mTOR inhibitors can synergistically inhibit Hodgkin lymphoma.

Patients and methods

During the dose-escalation study (ClinicalTrials.gov number: NCT01087554) with the HDAC inhibitor vorinostat and the mTOR inhibitor sirolimus (V+S), a patient with Hodgkin lymphoma refractory to nine prior therapies demonstrated a partial response (PR) lasting for 18.5 months, which promoted additional enrollment of patients with Hodgkin lymphoma as well as exploration of an alternative combination of vorinostat and mTOR inhibitor everolimus (V+E).

Results

A total of 40 patients with refractory Hodgkin lymphoma received V+S (n = 22) or V+E (n = 18). Patients received a median of five prior therapies, including brentuximab (n = 39), autologous stem cell transplantation (n = 26), and allogeneic stem cell transplantation (n = 12). The most frequent grade ≥3 treatment-related adverse event was thrombocytopenia in 55% and 67% of patients treated with V+S and V+E, respectively. Complete response was reported in 6 (27%) patients treated with V+S and 2 (11%) patients treated with V+E, and PR was reported in 6 patients (27%) treated with V+S and 4 (22%) patients treated with V+E (objective response rate of 55% and 33%, respectively). In summary, combined HDAC and mTOR inhibition had encouraging activity in heavily pretreated patients with relapsed/refractory Hodgkin lymphoma and warrants further investigation.

Conclusions

Combined HDAC and mTOR inhibition has salutary activity in patients with relapsed refractory Hodgkin lymphoma and warrants further investigation.

B cell receptor (BCR) signalling is an important pathway in diffuse large B cell lymphoma (DLBCL). In response to BCR triggering, normal and malignant B cells secrete the chemokines CCL3 and CCL4 to attract accessory cells to the tissue microenvironment. We measured CCL3 and CCL4 serum concentrations in 102 patients with newly diagnosed DLBCL by enzyme-linked immunosorbent assay, investigated their prognostic impact and validated our findings in an independent cohort of 51 patient samples. We also tested CCL3 and CCL4 secretion by DLBCL cells, and the influence of BTK inhibitors on the secretion of these chemokines. High CCL3 (≥40 pg/ml) serum concentrations correlated with higher international prognostic index, lactate dehydrogenase and β2 microglobulin, as did CCL4 (≥180 pg/ml) with advanced Ann Arbor stages. High CCL3 levels correlated with significantly shorter progression-free and overall survival. The in vitro studies demonstrated that activated B cell-like, but not germinal centre B cell-like DLBCL cells, secrete high levels of CCL3 and CCL4 after BCR triggering, which was exquisitely sensitive to BCR pathway inhibition. These findings support CCL3 and CCL4 protein concentrations as biomarkers for BCR pathway activation and prognosis in DLBCL.

Diffuse large B-cell lymphoma (DLBCL) is a heterogeneous entity of B-cell lymphoma. Cell-of-origin (COO) classification of DLBCL is required in routine practice by the World Health Organization classification for biological and therapeutic insights. Genetic subtypes uncovered recently are based on distinct genetic alterations in DLBCL, which are different from the COO subtypes defined by gene expression signatures of normal B cells retained in DLBCL. We hypothesize that classifiers incorporating both genome-wide gene-expression and pathogenetic variables can improve the therapeutic significance of DLBCL classification. To develop such refined classifiers, we performed targeted RNA sequencing (RNA-Seq) with a commercially available next-generation sequencing (NGS) platform in a large cohort of 418 DLBCLs. Genetic and transcriptional data obtained by RNA-Seq in a single run were explored by state-of-the-art artificial intelligence (AI) to develop a NGS-COO classifier for COO assignment and NGS survival models for clinical outcome prediction. The NGS-COO model built through applying AI in the training set was robust, showing high concordance with COO classification by either Affymetrix GeneChip microarray or the NanoString Lymph2Cx assay in 2 validation sets. Although the NGS-COO model was not trained for clinical outcome, the activated B-cell-like compared with the germinal-center B-cell-like subtype had significantly poorer survival. The NGS survival models stratified 30% high-risk patients in the validation set with poor survival as in the training set. These results demonstrate that targeted RNA-Seq coupled with AI deep learning techniques provides reproducible, efficient, and affordable assays for clinical application. The clinical grade assays and NGS models integrating both genetic and transcriptional factors developed in this study may eventually support precision medicine in DLBCL.