Transformed follicular lymphoma (tFL) originates from histological transformation of follicular lymphoma (FL), which is the most common indolent non-Hodgkin lymphoma. High-resolution genomic copy-number analysis previously identified frequent amplification of the 2p15-p16.1 locus in FL and tFL cases. The genes (i.e. BCL11A, PAPOLG, PUS10, and USP34) in this amplified locus have not been systematically investigated to date in terms of their role in FL pathogenesis or transformation to tFL. Here we investigated the relationship between amplification and expression of genes in 2p15-p16.1 as well as their expression after histological transformation. NCBI GEO SNP array and gene expression profile (GEP) data of tFL cases were analyzed to evaluate the relationship between amplification and mRNA expression. Moreover, transcript levels of these four genes in FL cases were compared with those of patient-matched tFL cases and normal B-cells. Amplification of the 2p15-p16.1 locus is associated with increased transcription of BCL11A and PAPOLG in tFL cases, of which the latter showed increased expression after histological transformation. Compared with the level in normal B-cells, PAPOLG was significantly overexpressed in FL cases, but expression levels of the other three genes did not show any significant difference. Altogether these results suggest that PAPOLG may be the most critical gene in terms of transformation to tFL.

Mantle cell lymphoma (MCL) is an aggressive B-cell non-Hodgkin lymphoma (NHL) subtype characterized by overexpression of CCND1 and SOX11 genes. It is generally associated with clinically poor outcomes despite recent improvements in therapeutic approaches. The genes associated with the development and prognosis of MCL are still largely unknown. Through whole transcriptome sequencing, we identified mRNAs, lncRNAs, and alternative transcripts differentially expressed in MCL cases compared with reactive tonsil B-cell subsets. CCND1, VCAM1, and VWF mRNAs, as well as MIR100HG and ROR1-AS1 lncRNAs, were among the top 10 most significantly overexpressed, oncogenesis-related transcripts. Survival analyses with each of the top upregulated transcripts showed that MCL cases with high expression of VWF mRNA and low expression of FTX lncRNA were associated with poor overall survival. Similarly, high expression of MSTRG.153013.3, an overexpressed alternative transcript, was associated with shortened MCL survival. Known tumor suppressor candidates (e.g., PI3KIP1, UBXN) were significantly downregulated in MCL cases. Top differentially expressed protein-coding genes were enriched in signaling pathways related to invasion and metastasis. Survival analyses based on the abundance of tumor-infiltrating immunocytes estimated with CIBERSORTx showed that high ratios of CD8+ T-cells or resting NK cells and low ratios of eosinophils are associated with poor overall survival in diagnostic MCL cases. Integrative analysis of tumor-infiltrating CD8+ T-cell abundance and overexpressed oncogene candidates showed that MCL cases with high ratio CD8+ T-cells and low expression of FTX or PCA3 can potentially predict high-risk MCL patients. WTS results were cross-validated with qRT-PCR of selected transcripts as well as linear correlation analyses. In conclusion, expression levels of oncogenesis-associated transcripts and/or the ratios of microenvironmental immunocytes in MCL tumors may be used to improve prognostication, thereby leading to better patient management and outcomes.

Predicting lung adenocarcinoma (LUAD) and Lung Squamous Cell Carcinoma (LUSC) risk status is a crucial step in precision oncology. In current clinical practice, clinicians, and patients are informed about the patient's risk group only with cancer staging. Several machine learning approaches for stratifying LUAD and LUSC patients have recently been described, however, there has yet to be a study that compares the integrated modeling of clinical and genetic data from these two lung cancer types. In our work, we used a prognostic prediction model based on clinical and somatically altered gene features from 1026 patients to assess the relevance of features based on their impact on risk classification. By integrating the clinical features and somatically mutated genes of patients, we achieved the highest accuracy; 93% for LUAD and 89% for LUSC, respectively. Our second finding is that new prognostic genes such as KEAP1 for LUAD and CSMD3 for LUSC and new clinical factors such as the site of resection are significantly associated with the risk stratification and can be integrated into clinical decision making. We validated the most important features found on an independent RNAseq dataset from NCBI GEO with survival information (GSE81089) and integrated our model into a user-friendly mobile application. Using this machine learning model and mobile application, clinicians and patients can assess the survival risk of their patients using each patient’s own clinical and molecular feature set.

Background: PRDM1 is a transcription factor that regulates differentiation and/or homeostasis of B and T lymphocytes. It may also play a role in natural killer (NK) cell homeostasis, and it is frequently inactivated in NK cell-derived lymphomas. Many target genes of PRDM1 have been reported; however, there is little insight into genes directly targeted by PRDM1 in NK cells. IL2 receptor signaling is crucial for many features of NK cell activation including sustained proliferation and survival. Here we evaluated whether CD25 (IL2Rα), a critical gene involved in NK cell activation, is transcriptionally repressed by PRDM1 in activated human NK cells. Methods: ChIP-Seq was applied on normal primary NK cells activated using a special co-culture system that involved genetically engineered NK cell target cells (i.e. K562-Cl9-mb21) to determine genomic locations occupied with PRDM1. PRDM1 binding site screen in CD25 was computationally performed using two different ChIP-Seq peak callers. Two malignant NK cell lines (NK92 and KHYG1) were transduced with a retroviral construct that co-expresses PRDM1α and GFP. CD25 transcription expression was evaluated with DNA microarray, RNA-Seq and RT-qPCR on GFP-sorted NK cells 48h post-transduction. CD25 expression was stably knocked-down with two different shRNAs in NK92 and KHYG1 cell lines. CD25 was ectopically expressed in primary human NK cells activated through coculturing with K562-Cl9-mb21. GFP competition assays were performed on CD25 shRNA-transduced NK cell lines or primary NK cells with ectopic CD25 expression in limiting IL2 concentrations, respectively. Results: PRDM1 occupancy on the 1st intron of CD25 was detected with CisGenome and MACS softwares in activated primary NK cells. Direct binding of PRDM1 on CD25 was further validated by ChIP-qPCR in normal activated NK cells. Two PRDM1α-transduced PRDM1-nonexpressing NK cell lines showed significant transcriptional repression of CD25 by DNA microarray and RNA-Seq, the repression of which was cross-validated with RT-qPCR. Stable knock-down of CD25 inhibited growth of two NK cell lines cultured in limiting IL2 concentrations. By contrast, ectopic expression of CD25 resulted in positive selection of ex vivo cultured primary NK cells. Conclusions: Altogether these results establish CD25 as a direct transcriptional target of PRDM1 in human NK cells. These observations provide additional support for the role of PRDM1 in termination of NK cell activation and growth, with implications on neoplastic transformation or NK cell function when it is deregulated. Disclosures No relevant conflicts of interest to declare.