Dehzangi, I. | Search bioRxiv

bioRxiv2026

TEDEdb: a large-scale resource and multi-cohort analysis of transposable element differential expression in cancer

The human genome consists of nearly 50% repetitive DNA, referred to for decades as "junk DNA". These repetitive sequences, usually under the strict control of epigenetic silencing, have been observed to be aberrantly expressed in cancer. Some of these expressed sequences, e.g., transposable elements (TEs), can induce innate immune responses when de-repressed following treatment with epigenetic therapies. As a result, epigenetic therapy has been suggested to augment cancer therapies. TEs are traditionally ignored in most RNA-seq studies and their expression is often excluded from publicly available data sources. Thus, the vast amount of publicly available RNA-seq data is an untapped resource for exploring the role of TE expression in cancer and cancer treatment. Here, we present a uniform re-analysis of over 7,000 RNA-seq samples, encompassing more than 2,000 differential expression experiments across 220 cancer cell lines and 700 drug treatments. We observed that TE expression is more prone to batch effects than gene expression alone, necessitating the use of meta-analysis techniques to probe the dataset for global trends. We confirm that DNMTi and HDACis are powerful inducers of TEs. We also show that non-epigenetic compounds such as CDK and topoisomerase inhibitors can also induce robust up-regulation of transposable elements and confirm that this TE induction is consistent with viral mimicry response. We make all of the reprocessed data, web application, and database publicly available at: https://dataexplorer.coriell.org/TEDEdb/

cancer biology↗

bioRxiv2025

Population analysis and immunologic landscape of melanoma in people living with HIV

PurposeTo dissect the clinical and immunological features of people living with HIV (PLWH) diagnosed with melanoma, who have consistently shown worse outcomes than HIV-negative individuals (PLw/oH) with the same cancer. Experimental DesignWe analyzed electronic health records from 1,087 PLWH and 394,437 PLw/oH with melanoma. Demographic and clinical characteristics were compared. Spatial immune transcriptomics (72 immune-related genes) was performed on melanoma tumor samples (n=11), with downstream validation using multiplex immunofluorescence (n=15 PLWH, n=14 PLw/oH). ResultsPLWH were diagnosed at a younger age, had greater representation of Hispanic and Black individuals, and showed reduced survival. They also had a markedly increased risk of brain metastases. PLWH experienced significant delays in initiating immune checkpoint inhibitor (ICI) therapy and had worse post-ICI survival, even after balancing covariates. Spatial transcriptomics revealed a more immunosuppressive tumor microenvironment in PLWH, with increased transcription of immune checkpoints (PD1, LAG3) and reduced antigen-presentation markers (HLA-DRB, B2M), with distinct spatial distributions in tumors and surrounding microenvironments. Multiplex immunofluorescence demonstrated features of an exhausted CD8 T cell compartment, including enrichment of PD1intLAG3- and PD1intLAG3 subpopulations, and a significant accumulation of myeloid-derived suppressor cells (CD11b HLA-DR- CD33). ConclusionsMelanoma in PLWH is associated with distinct clinical and immunological features, including delayed ICI treatment, reduced survival, and an immunosuppressive microenvironment with exhausted CD8 T cells and expanded myeloid-derived suppressor cells. These findings suggest that chronic HIV infection may impair antitumor immunity in melanoma. Targeting the pathways identified here may improve therapeutic responses and outcomes in this population. Statement of translational relevanceThis study reveals critical barriers to effective melanoma treatment in people living with HIV (PLWH). Despite receiving immune checkpoint inhibitors (ICIs), PLWH face delayed therapy initiation, a greater likelihood of brain metastases, and significantly higher long-term mortality, even after adjusting for demographic covariates. Transcriptional immune profiling further uncovers a tumor microenvironment enriched in immunosuppressive myeloid-derived suppressor cells and CD8 T cell populations with features of exhaustion. These findings suggest that poorer outcomes in PLWH stem not only from delayed care, but also from distinct targetable mechanisms of immune dysfunction. For example, strategies to reverse MDSC accumulation in the tumor or tailored ICI regimens could enhance immune responsiveness and improve treatment efficiency. By defining the clinical and immunological features of this population, this work highlights opportunities for precision immunotherapy tailored to PLWH with melanoma, with direct implications for improving survival and reducing disparities.

cancer biology↗

bioRxiv2023

Deep Learning for Protein Peptide bindingPrediction: Incorporating Sequence, Structural andLanguage Model Features

Protein-peptide interactions play a crucial role in various cellular processes and are implicated in abnormal cellular behaviors leading to diseases such as cancer. Therefore, understanding these interactions is vital for both functional genomics and drug discovery efforts. Despite a significant increase in the availability of protein-peptide complexes, experimental methods for studying these interactions remain laborious, time-consuming, and expensive. Computational methods offer a complementary approach but often fall short in terms of prediction accuracy. To address these challenges, we introduce PepCNN, a deep learning-based prediction model that incorporates structural and sequence-based information from primary protein sequences. By utilizing a combination of half-sphere exposure, position specific scoring matrices, and pre-trained transformer language model, PepCNN outperforms state-of-the-art methods in terms of specificity, precision, and AUC. The PepCNN software and datasets are publicly available at https://github.com/abelavit/PepCNN.git.

bioinformatics↗