Prognostic Biomarkers for Hodgkin Lymphoma

Pathobiological Features and Therapeutic Opportunities Linked to TNF Family Member Expression in Classic Hodgkin Lymphoma

The tumor necrosis factor (TNF) family, which includes 19 ligands and 29 receptors, influences cellular proliferation, differentiation, and apoptosis. The TNF family plays a crucial role in the pathogenesis of Hodgkin lymphoma (HL), particularly through its influence on the tumor microenvironment (TME). Hodgkin Reed-Sternberg (HRS) cells, the hallmark of classic HL (cHL), exhibit overexpression of TNF receptor family members such as CD30 and CD40. Given the critical roles of CD30 and CD40 in the survival and proliferation of HRS cells within the TME, targeting these TNF receptors represents a promising therapeutic strategy; therapies that target CD30 have already shown efficacy in clinical settings. The programmed death-1 (PD-1)/programmed death-ligand 1 (PD-L1) axis plays a crucial role in immune evasion by HRS cells, which express PD-L1 that interacts with PD-1 on T cells, leading to T cell exhaustion and a diminished immune response against the tumor. By blocking this interaction, checkpoint inhibitors such as nivolumab and pembrolizumab have demonstrated high response rates in patients with cHL, particularly in those who have not responded to conventional therapies. The integration of immune checkpoint inhibitors (ICIs) with standard chemotherapy regimens has improved outcomes for patients with advanced-stage cHL. By understanding how TNF signaling interacts with immune checkpoints, researchers can design more effective treatment regimens that simultaneously target multiple pathways. Combining TNF inhibitors with checkpoint blockade therapies may enhance the overall anti-tumor response by addressing both direct tumor signaling and the immune evasion mechanisms employed by tumor cells.

GeneRaMeN enables integration, comparison, and meta-analysis of multiple ranked gene lists to identify consensus, unique, and correlated genes

High-throughput experiments often produce ranked gene outputs, with forward genetic screening being a notable example. While there are various tools for analyzing individual datasets, those that perform comparative and meta-analytical examination of such ranked gene lists remain scarce. Here, we introduce Gene Rank Meta Analyzer (GeneRaMeN), an R Shiny tool utilizing rank statistics to facilitate the identification of consensus, unique, and correlated genes across multiple hit lists. We focused on two key topics to showcase GeneRaMeN: virus host factors and cancer dependencies. Using GeneRaMeN 'Rank Aggregation', we integrated 24 published and new flavivirus genetic screening datasets, including dengue, Japanese encephalitis, and Zika viruses. This meta-analysis yielded a consensus list of flavivirus host factors, elucidating the significant influence of cell line selection on screening outcomes. Similar analysis on 13 SARS-CoV-2 CRISPR screening datasets highlighted the pivotal role of meta-analysis in revealing redundant biological pathways exploited by the virus to enter human cells. Such redundancy was further underscored using GeneRaMeN's 'Rank Correlation', where a strong negative correlation was observed for host factors implicated in one entry pathway versus the alternate route. Utilizing GeneRaMeN's 'Rank Uniqueness', we analyzed human coronaviruses 229E, OC43, and SARS-CoV-2 datasets, identifying host factors uniquely associated with a defined subset of the screening datasets. Similar analyses were performed on over 1000 Cancer Dependency Map (DepMap) datasets spanning 19 human cancer types to reveal unique cancer vulnerabilities for each organ/tissue. GeneRaMeN, an efficient tool to integrate and maximize the usability of genetic screening datasets, is freely accessible via https://ysolab.shinyapps.io/GeneRaMeN.

Hounsfield units and fractal dimension (test HUFRA) for determining PET positive/negative lymph nodes in pediatric Hodgkin's lymphoma patients

Objectives

We had developed a method that can help detect and identify lymph nodes affected by the neoplastic process. Our group evaluated the fractal dimension (FD) and X-ray attenuation (XRA) of lymph nodes in HL and compared to their metabolic activity as measured by 18F-FDG-PET examination.

Methods

The training set included 72 lymph nodes from 31 consecutive patients, and the tested set of 71 lymph nodes from next 19 patients. The measurement of FD of each lymph node was performed before the start of therapy using original software. X-ray attenuation (XRA) expressed in HU (Hounsfield Units) from CT scans was compared with the metabolic activity of the lymphatic nodes, measured by 18F-FDG-PET examination.

Results

Significant differences were observed between XRA_max and FD_max values in assessing the PET(+) and PET(-) nodes. All nodes were scored from 0 to 2. The HUFRA test properly qualified 95% with a score of 2 and 0 points as PET(+) or PET(-).

Conclusion

The HUFRA test can differentiate about 70–80% of lymph nodes as PET(+) or PET(-) based solely on the CT examination. It can be useful in patients who were not subjected to 18FFDG-PET/CT examination before the treatment, or who had an unreliable result of 18F-FDG-PET/CT with further research requirements.

WebHERV: A Web Server for the Computational Investigation of Gene Expression Associated With Endogenous Retrovirus-Like Sequences

More than eight percent of the human genome consists of human endogenous retroviruses (HERVs). Typically, the expression of HERVs is repressed, but varying activities of HERVs have been observed in diseases ranging from cancer to neuro-degeneration. Such activities can include the transcription of HERV-derived open reading frames, which can be translated into proteins. However, as a consequence of mutations that disrupt open reading frames, most HERV-like sequences have lost their protein-coding capacity. Nevertheless, these loci can still influence the expression of adjacent genes and, hence, mediate biological effects. Here, we present WebHERV (http://calypso.informatik.uni-halle.de/WebHERV/), a web server that enables the computational prediction of active HERV-like sequences in the human genome based on a comparison of genome coordinates of expressed sequences uploaded by the user and genome coordinates of HERV-like sequences stored in the specialized key-value store DRUMS. Using WebHERV, we predicted putative candidates of active HERV-like sequences in Hodgkin lymphoma (HL) cell lines, validated one of them by a modified SMART (switching mechanism at 5′ end of RNA template) technique, and identified a new alternative transcription start site for cytochrome P450, family 4, subfamily Z, polypeptide 1 (CYP4Z1).