Follicular lymphoma comprises germinal center-like and memory-like molecular subtypes with prognostic significance.

A robust prognostic and biological classification for newly diagnosed follicular lymphoma (FL) using molecular profiling remains challenging. FL tumors from patients treated in the RELEVANCE trial with rituximab-chemotherapy (R-chemo) or rituximab-lenalidomide (R2) were analyzed using RNA-sequencing, DNA-sequencing, immunohistochemistry (IHC) and/or fluorescence in situ hybridization. Unsupervised gene clustering identified two gene expression signatures (GS) enriched with normal memory (MEM) B-cells and germinal center (GC) B-cells signals, respectively. These two GS were combined into a 20-genes predictor (FL20) to classify patients into MEM-like (n=160) or GC-like (n=164) subtypes, which also displayed different mutational profiles. In the R-chemo arm, MEM-like patients had significantly shorter progression free survival (PFS) than GC-like patients (HR=2.13; p=0.0023), and this prognostic correlation remained significant in a multivariable model including FLIPI (p=0.005). In the R2 arm, both subtypes had comparable PFS, demonstrating a R2 benefit over R-chemo for MEM-like patients (HR=0.54; p=0.011). The prognostic value of FL20 was validated in an independent FL cohort with R-chemo treatment (GSE119214 (n=137)). An IHC algorithm (FLCM) using FOXP1, LMO2, CD22 and MUM1 antibodies was developed with significant prognostic correlation with FL20 in a training set of RELEVANCE (n=264) patients, which was then validated in a different set of patients (n=116). These data indicate that FL tumors can be classified into MEM-like and GC-like subtypes that are biologically distinct and clinically different in risk profile. The FLCM assay can be used in routine clinical practice to identify MEM-like FL patients who might benefit from therapies other than R-chemo, such as the R2 combination. ClinicalTrials.gov identifier: RELEVANCE: NCT01476787 and NCT01650701 INTRODUCTION.

The application of network label propagation to rank biomarkers in genome-wide Alzheimer's data.

BACKGROUND: Ranking and identifying biomarkers that are associated with disease from genome-wide measurements holds significant promise for understanding the genetic basis of common diseases. The large number of single nucleotide polymorphisms (SNPs) in genome-wide studies (GWAS), however, makes this task computationally challenging when the ranking is to be done in a multivariate fashion. This paper evaluates the performance of a multivariate graph-based method called label propagation (LP) that efficiently ranks SNPs in genome-wide data. RESULTS: The performance of LP was evaluated on a synthetic dataset and two late onset Alzheimer's disease (LOAD) genome-wide datasets, and the performance was compared to that of three control methods. The control methods included chi squared, which is a commonly used univariate method, as well as a Relief method called SWRF and a sparse logistic regression (SLR) method, which are both multivariate ranking methods. Performance was measured by evaluating the top-ranked SNPs in terms of classification performance, reproducibility between the two datasets, and prior evidence of being associated with LOAD.On the synthetic data LP performed comparably to the control methods. On GWAS data, LP performed significantly better than chi squared and SWRF in classification performance in the range from 10 to 1000 top-ranked SNPs for both datasets, and not significantly different from SLR. LP also had greater ranking reproducibility than chi squared, SWRF, and SLR. Among the 25 top-ranked SNPs that were identified by LP, there were 14 SNPs in one dataset that had evidence in the literature of being associated with LOAD, and 10 SNPs in the other, which was higher than for the other methods. CONCLUSION: LP performed considerably better in ranking SNPs in two high-dimensional genome-wide datasets when compared to three control methods. It had better performance in the evaluation measures we used, and is computationally efficient to be applied practically to data from genome-wide studies. These results provide support for including LP in the methods that are used to rank SNPs in genome-wide datasets.

Transcriptomic classification of diffuse large B-cell lymphoma identifies a high-risk activated B-cell-like subpopulation with targetable MYC dysregulation.

Immunochemotherapy has been the mainstay of treatment for newly diagnosed diffuse large B-cell lymphoma (ndDLBCL) yet is inadequate for many patients. In this work, we perform unsupervised clustering on transcriptomic features from a large cohort of ndDLBCL patients and identify seven clusters, one called A7 with poor prognosis, and develop a classifier to identify these clusters in independent ndDLBCL cohorts. This high-risk cluster is enriched for activated B-cell cell-of-origin, low immune infiltration, high MYC expression, and copy number aberrations. We compare and contrast our methodology with recent DLBCL classifiers to contextualize our clusters and show improved prognostic utility. Finally, using pre-clinical models, we demonstrate a mechanistic rationale for IKZF1/3 degraders such as lenalidomide to overcome the low immune infiltration phenotype of A7 by inducing T-cell trafficking into tumors and upregulating MHC I and II on tumor cells, and demonstrate that TCF4 is an important regulator of MYC-related biology in A7.

Multiomic analysis identifies a high-risk signature that predicts early clinical failure in DLBCL.

Recent genetic and molecular classification of DLBCL has advanced our knowledge of disease biology, yet were not designed to predict early events and guide anticipatory selection of novel therapies. To address this unmet need, we used an integrative multiomic approach to identify a signature at diagnosis that will identify DLBCL at high risk of early clinical failure. Tumor biopsies from 444 newly diagnosed DLBCL were analyzed by WES and RNAseq. A combination of weighted gene correlation network analysis and differential gene expression analysis was used to identify a signature associated with high risk of early clinical failure independent of IPI and COO. Further analysis revealed the signature was associated with metabolic reprogramming and identified cases with a depleted immune microenvironment. Finally, WES data was integrated into the signature and we found that inclusion of ARID1A mutations resulted in identification of 45% of cases with an early clinical failure which was validated in external DLBCL cohorts. This novel and integrative approach is the first to identify a signature at diagnosis, in a real-world cohort of DLBCL, that identifies patients at high risk for early clinical failure and may have significant implications for design of therapeutic options.

Prolyl-tRNA synthetase as a novel therapeutic target in multiple myeloma.

Multiple myeloma (MM) is a plasma cell malignancy characterised by aberrant production of immunoglobulins requiring survival mechanisms to adapt to proteotoxic stress. We here show that glutamyl-prolyl-tRNA synthetase (GluProRS) inhibition constitutes a novel therapeutic target. Genomic data suggest that GluProRS promotes disease progression and is associated with poor prognosis, while downregulation in MM cells triggers apoptosis. We developed NCP26, a novel ATP-competitive ProRS inhibitor that demonstrates significant anti-tumour activity in multiple in vitro and in vivo systems and overcomes metabolic adaptation observed with other inhibitor chemotypes. We demonstrate a complex phenotypic response involving protein quality control mechanisms that centers around the ribosome as an integrating hub. Using systems approaches, we identified multiple downregulated proline-rich motif-containing proteins as downstream effectors. These include CD138, transcription factors such as MYC, and transcription factor 3 (TCF3), which we establish as a novel determinant in MM pathobiology through functional and genomic validation. Our preclinical data therefore provide evidence that blockade of prolyl-aminoacylation evokes a complex pro-apoptotic response beyond the canonical integrated stress response and establish a framework for its evaluation in a clinical setting.

Large-scale Analyses of Disease Biomarkers and Apremilast Pharmacodynamic Effects.

Finding biomarkers that provide shared link between disease severity, drug-induced pharmacodynamic effects and response status in human trials can provide number of values for patient benefits: elucidating current therapeutic mechanism-of-action, and, back-translating to fast-track development of next-generation therapeutics. Both opportunities are predicated on proactive generation of human molecular profiles that capture longitudinal trajectories before and after pharmacological intervention. Here, we present the largest plasma proteomic biomarker dataset available to-date and the corresponding analyses from placebo-controlled Phase III clinical trials of the phosphodiesterase type 4 inhibitor apremilast in psoriasis (PSOR), psoriatic arthritis (PsA), and ankylosing spondylitis (AS) from 526 subjects overall. Using approximately 150 plasma analytes tracked across three time points, we identified IL-17A and KLK-7 as biomarkers for disease severity and apremilast pharmacodynamic effect in psoriasis patients. Combined decline rate of KLK-7, PEDF, MDC and ANGPTL4 by Week 16 represented biomarkers for the responder subgroup, shedding insights into therapeutic mechanisms. In ankylosing spondylitis patients, IL-6 and LRG-1 were identified as biomarkers with concordance to disease severity. Apremilast-induced LRG-1 increase was consistent with the overall lack of efficacy in ankylosing spondylitis. Taken together, these findings expanded the mechanistic knowledge base of apremilast and provided translational foundations to accelerate future efforts including compound differentiation, combination, and repurposing.

Ozanimod (RPC1063), a selective S1PR1 and S1PR5 modulator, reduces chronic inflammation and alleviates kidney pathology in murine systemic lupus erythematosus.

Ozanimod (RPC1063) is a specific and potent small molecule modulator of the sphingosine 1-phosphate receptor 1 (S1PR1) and receptor 5 (S1PR5), which has shown therapeutic benefit in clinical trials of relapsing multiple sclerosis and ulcerative colitis. Ozanimod and its active metabolite, RP-101075, exhibit a similar specificity profile at the S1P receptor family in vitro and pharmacodynamic profile in vivo. The NZBWF1 mouse model was used in therapeutic dosing mode to assess the potential benefit of ozanimod and RP-101075 in an established animal model of systemic lupus erythematosus. Compared with vehicle-treated animals, ozanimod and RP-101075 reduced proteinuria over the duration of the study and serum blood urea nitrogen at termination. Additionally, ozanimod and RP-101075 reduced kidney disease in a dose-dependent manner, as measured by histological assessment of mesangial expansion, endo- and exo-capillary proliferation, interstitial infiltrates and fibrosis, glomerular deposits, and tubular atrophy. Further exploration into gene expression changes in the kidney demonstrate that RP-101075 also significantly reduced expression of fibrotic and immune-related genes in the kidneys. Of note, RP-101075 lowered the number of plasmacytoid dendritic cells, a major source of interferon alpha in lupus patients, and reduced all B and T cell subsets in the spleen. Given the efficacy demonstrated by ozanimod and its metabolite RP-101075 in the NZBWF1 preclinical animal model, ozanimod may warrant clinical evaluation as a potential treatment for systemic lupus erythematosus.

Application of a spatially-weighted Relief algorithm for ranking genetic predictors of disease.

BACKGROUND: Identification of genetic variants that are associated with disease is an important goal in elucidating the genetic causes of diseases. The genetic patterns that are associated with common diseases are complex and may involve multiple interacting genetic variants. The Relief family of algorithms is a powerful tool for efficiently identifying genetic variants that are associated with disease, even if the variants have nonlinear interactions without significant main effects. Many variations of Relief have been developed over the past two decades and several of them have been applied to single nucleotide polymorphism (SNP) data. RESULTS: We developed a new spatially weighted variation of Relief called Sigmoid Weighted ReliefF Star (SWRF*), and applied it to synthetic SNP data. When compared to ReliefF and SURF*, which are two algorithms that have been applied to SNP data for identifying interactions, SWRF* had significantly greater power. Furthermore, we developed a framework called the Modular Relief Framework (MoRF) that can be used to develop novel variations of the Relief algorithm, and we used MoRF to develop the SWRF* algorithm. CONCLUSIONS: MoRF allows easy development of new Relief algorithms by specifying different interchangeable functions for the component terms. Using MORF, we developed a new Relief algorithm called SWRF* that had greater ability to identify interacting genetic variants in synthetic data compared to existing Relief algorithms.